Other Top-tier International Conference Papers
- MobiSys 1, RTAS 7, DAC 1, EMSOFT 2, ECRTS 1, ICCPS 2, e-Energy 1, SAC 2
Top-tier International Journal Papers
- TPDS 7, TC 4, TSE 1, IoT-J 2, TECS 1, TVCG 1, RTSJ 2, JSS 8, TVT 1, FGCS 1, TII 1
A mixed-criticality (MC) system is a computational platform shared by tasks with two or more safety-critical levels. An important research topic related to MC systems is designing scheduling algorithms that can satisfy the computation requirements of tasks with different criticality levels. Numerous studies have focused on this topic, but only a few have considered parallel tasks. To address the research gap, we propose a dual- criticality scheduling algorithm based on federated scheduling for parallel tasks with Directed Acyclic Graph (DAG) structures. We particularly focus on the task set in which each task has a deadline longer than its release period. To the best of our knowledge, our work is the first that does not assume the constrained- or implicit-deadline in the MC DAG task model. In addition to simulation experiments, we demonstrate that our algorithm has a capacity augmentation bound of 4, providing a quantitative worst-case performance guarantee for our algorithm.
Vision-centric Bird's Eye View (BEV) perception
has gained popularity for enhancing the situational awareness
of autonomous vehicles (AVs). It uses multiple cameras to
create a 360 view, capturing essential details for the vehicle's
navigation and decision-making. However, reducing the end-to-
end (e2e) BEV perception latency without sacrificing accuracy
is challenging due to the lack of co-optimization of message
communication and object detection. Previous works either
compress the dense detection model to reduce computation, which
can hurt accuracy and assume images are well synchronized, or
focus on worst-case communication delay without considering
detection characteristics.
To address this challenge, we propose RT-BEV, the first
framework designed to co-optimize message communication and
object detection for improving real-time e2e BEV perception
without sacrificing accuracy. Our main insight lies in generat-
ing traffic environment- and context-aware Regions of Interest
(ROIs) for AV safety, combined with ROI-aware message commu-
nication. RT-BEV features an ROI-aware Camera Synchronizer
that adaptively determines message groups and allowable delays
based on ROIs' coverage. Additionally, we develop the ROIs
Generator to model context-aware ROIs and a Feature Split
& Merge component to handle variable-sized ROIs effectively.
Furthermore, a Time Predictor forecasts timelines for ROIs'
processing, and a Coordinator jointly optimizes both latency
and accuracy for the entire e2e pipeline. We have implemented
RT-BEV in a ROS-based BEV perception pipeline and evaluated
it with the nuScenes dataset. RT-BEV significantly enhances real-
time BEV perception by reducing average e2e latency by 1.5x,
maintaining high mean Average Precision (mAP), doubling the
number of processed frames, and improving the frame efficiency
score (FES) by 2.9× compared to existing approaches. Moreover,
RT-BEV reduces the worst-case e2e latency by 19.3x.
While most existing battery cell balancing approaches were posttreatment (i.e., handling diverse voltage levels originating from different battery cell status), a pretreatment approach, called reconfiguration-assisted charging (RAC), was developed, which dynamically attaches a proper number of resistor arrays to each group of battery cells with similar status, preventing battery cell imbalance; note that this pretreatment approach can be used orthogonally with existing posttreatment approaches such as active/passive balancing. Relaxing its impractical assumptions of RAC (e.g., all necessary resistor arrays are deployed in the target system), this article proposes RAC+ , which realizes its practical and efficient use for the pretreatment concept of RAC. The experiment results demonstrate that RAC+ achieves the same balancing performance as RAC while reducing the number of required resistors by 69% compared to RAC . The extensive experiment results also show that RAC+ is not only robust to various charging environments, but also proven to be effective in terms of minimizing power loss.
ISSN: 1551-3203
Mixed-Criticality (MC) systems have successfully overcome the limitation of traditional real-time systems based on pessimistic Worst-Case Execution Times (WCETs), by using different WCETs depending on different criticalities. One of the important yet unsolved problems of current MC systems is to achieve two goals (G1 and G2) for low-criticality tasks without compromising timing guarantees for high-criticality tasks: (G1) providing a certain (degraded) level of timing guarantees for all low-criticality tasks; (G2) while maximizing the fully-serviced (non-degraded) execution of low-criticality tasks. To address the problem, we propose IMC-PnG, an MC scheduling framework, which employs two salient features: a task-level virtual-deadline assignment under the imprecise computing model with efficient resource utilization (supporting G1), and an online scheduling algorithm that dynamically changes criticality levels of individual tasks at runtime (supporting G2). In simulation results with random workloads, we showed that IMC-PnG has up to 12.10% higher schedulability and up to 42.10% higher runtime performance (measured by the fully-serviced ratio of low-criticality tasks) than the existing approaches.
ISSN: 0167-739X
Targeting a MOT (Multi-Object Tracking) system with multiple MOT tasks, this paper develops Batch-MOT, the first system design that achieves both (G1) timing guarantee and (G2) accuracy maximization, by utilizing batch execution that allows multiple DNN executions to perform simultaneously in a single DNN inference, resulting in significantly decreased execution time without accuracy loss. To this end, we propose an adaptable scheduling framework that allows run-time execution behaviors deviated from our base scheduling algorithm (i.e., non-preemptive fixed-priority scheduling) without compromising G1. Based on the adaptable framework, we then develop (i) a run-time batching mechanism that finds and executes a batch set of MOT tasks and (ii) a run-time idling mechanism that waits for future releases of MOT tasks for batch execution. Both run-time mechanisms can achieve G1 and G2 without incurring high run-time overhead, as they systematically exploit the run-time execution behaviors allowed by the adaptive framework. Our evaluation conducted with a real-world data set demonstrates the effectiveness of Batch-MOT in improving tracking accuracy while providing a timing guarantee, compared to the state-of-the-art real-time MOT system for multiple MOT tasks.
As the application scope of DNNs executed on microcontroller units (MCUs) extends to time-critical systems, it becomes impor- tant to ensure timing guarantees for increasing demand of DNN inferences. To this end, this paper proposes RT-MDM, the first Real- T ime scheduling framework for M ultiple D NN tasks executed on an M CU using external memory. Identifying execution-order depen- dencies among segmented DNN models and memory requirements for parallel execution subject to the dependencies, we propose (i) a segment-group-based memory management policy that achieves iso- lated memory usage within a segment group and sharded memory usage across different segment groups, and (ii) an intra-task sched- uler specialized for the proposed policy. Implementing RT-MDM on an actual system and optimizing its parameters for DNN segmenta- tion and segment-group mapping, we demonstrate the effectiveness of RT-MDM in accommodating more DNN tasks while providing their timing guarantees
This paper considers the real-time scheduling of a parallel task with reclaiming computing resources, which can be utilized for soft real-time tasks or switching to low-energy mode to save energy. Existing works allocate a rectangular piece of computing resources based on the worst-case characterizations of the task to guarantee the deadline, which inherently incurs severe resource wasting due to coarse-grained resource allocation. To address this resource-wasting problem, this paper proposes the ladder-like resource allocation (i.e., a series of rectangular pieces of computing resources). To characterize the ladder-like resource allocation, we present two concepts called resource distribution and allocation vector, which serve as the interfaces between hard and soft real-time tasks. For the former, we derive schedulability tests under the given two interfaces; for the latter, we discuss the methods of determining the two interfaces to reclaim computing resources. This paper is the first work to fully explore the concept of ladder-like resource allocation and its potential consequences on computing resources, soft real-time tasks, and energy. Experiments demonstrate that the proposed approach can effectively reclaim more computing resources than existing approaches while maintaining hard real-time guarantees.
ISSN: 0922-6443
The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity.
Unlike existing accuracy-centric multi-object tracking (MOT), MOT subsystems for autonomous vehicles (AVs) must accurately perceive the surrounding conditions of the vehicle and timely deliver the perception results to the control subsystems before losing stability. In this paper, we proposed MOT-AS (Multi-Object Tracking systems capturing Accuracy and Stability), a novel handover-aware MOT execution and scheduling framework tailored for AVs with multi-cameras, which aims to maximize tracking accuracy without sacrificing system stability. Given the resource limitations inherent to AVs, MOT-AS partitions the handover-aware MOT execution into two distinct sub-executions: tracking handover objects that move across multiple cameras (referred to as global association) and those that move within a single camera (termed local association). It selectively performs the global association only when necessary and carries out local association with multiple execution options to explore the trade-off between accuracy and stability. Building upon MOT-AS, we developed a new scheduling framework encompassing a new MOT task model, offline stability analysis, and online scheduling algorithm to maximize accuracy without compromising stability. We implemented MOT-AS on both high-end and embedded GPU platforms using the Nuscenes dataset, demonstrating enhanced tracking accuracy and stability over conventional MOT systems, irrespective of their handover considerations.
Among CT (Computed Tomography) techniques that produce crosssection images by acquiring X-ray data from multiple angles around the individual, CBCT (Cone Beam CT) that collects the data through cone beam is popular due to its potential to reduce radiation risks. Although employing low-dose and sparse-view CBCT is a cornerstone approach to reducing radiation risks, reconstructing CBCT from noisy and sparsely-acquired cone beam scans often result in significant artifacts due to ill-posed inverse problem. In this paper, we aim to suppress the artifacts derived from reconstructing CBCT. To this end, we target the well-known existing approach ASD-POCS (Adaptive Steepest Descent with Projections Onto Convex Sets), focus on its downside of potentially introducing over-smoothing near edges and removing intricate fine structures, and develop an advanced ASD-POCS strategy that addresses the downside by leveraging prior image information. Our approach to integrating the prior information not only maintains structural integrity (without compromising edge detail or erasing fine structures) but also diminishes artifacts, thereby counteracting noise and blur to a significant extent. Our solution underwent stringent testing using both simulated data and publicly available datasets. The testing results demonstrate that the proposed technique is robust to the prevailing challenges in sparse-view CBCT reconstruction, skillfully mitigating artifacts (compared to contemporary state-of-the-art methods) while safeguarding intricate structures.
Although blockchain technology is being increas- ingly utilized across various fields, the challenge of providing timing guarantees for transactions remains unmet, which is an obstacle in implementing blockchain solutions for time-sensitive applications such as high-frequency trading and real-time pay- ments. In this paper, we propose the first solution to achieve a timing guarantee on blockchain. To this end, we raise and address two issues for timely transactions on a blockchain: (a) architectural support, and (b) real-time scheduling principles spe- cialized for blockchain. For (a), we modify an existing blockchain network, offering an interface to preferentially select the transac- tions with the earliest deadlines. We then extend the blockchain network to provide the flexibility of the number of generated blocks at a single block time. Under such architectural supports, we achieve (b) with three steps. First, to resolve a discrepancy between a periodic request of a transaction-generating node and the corresponding arrival on a block-generating node, we translate the former into the latter, which eases the modeling of the transaction load imposed on the blockchain network. Second, we derive a schedulability condition of the modeled transaction load, which guarantees no missed deadline for all transactions under a work-conserving deadline-based scheduling policy. Last, we develop a lazy scheduling policy and its condition, which reduces the number of generated blocks without compromising the degree of timing guarantees for the work-conserving policy. By implementing RT-blockchain on top of an existing open- source blockchain project, we demonstrate the effectiveness of the proposed scheduling principles with architectural supports in not only ensuring timely transactions but also reducing the number of generating blocks.
Despite the physical advance of an existing single-cell battery system, mobile users are still suffering from low battery anxiety. With a careful analysis of users’ battery usage behavior collected for 19,855 hours, we propose a heterogeneous battery system, MixMax, consisting of three complementary battery types tailored to minimizing the low battery time. While composing a heterogeneous battery system opens up a chance to simultaneously improve the capacity and the charging speed, one must face non-trivial challenges to determine the ratio of enclosed batteries and charge/discharge policies during the run-time. They are highly dependent on each other, which entails almost infinite candidates for the choice. MixMax gracefully unwinds the dependencies as it formulates the decision-making problem into an optimization problem and decomposes it into multiple sub-problems instead. To evaluate MixMax, we fabricate coin-cell batteries and experiment with them to model an accurate battery emulator which sophisticatedly reproduces the dynamics of battery systems. Our experimental results demonstrate that MixMax can reduce the low battery time by up to 24.6% without compromising capacity, volume, weight, and more importantly, users’ battery usage behavior. In addition, we prototype MixMax on a smartphone, presenting the practicality of MixMax on mobile systems.
This paper aims at developing a tight schedulability analysis for real-time global gang scheduling, in which threads of each task subject to timing requirements are assigned to multiple processors in parallel (i.e., following the rigid gang task model). Focusing on the RTA (Response Time Analysis) framework known to exhibit high schedulability performance for other task models, we address two following issues: i) how to generalize the existing RTA framework to gang scheduling and utilize existing RTA components of other task models for the generalized framework, and ii) how to incorporate important characteristics of gang scheduling into the RTA framework in a systematic way to minimize the framework's pessimism in judging schedulability. By addressing the issues, our RTA framework enables to derive tight schedulability analysis for EDF, FP and potentially more scheduling algorithms for real-time global gang scheduling. Also, our simulation results demonstrate that the proposed RTA framework outperforms/complements existing studies for real-time global/non-global gang scheduling, in terms of schedulability performance.
Due to its efficient and predictable utilization of modern computing units, recent studies have paid attention to gang scheduling in which all threads of a real-time task should be concurrently executed on different processors. However, the studies have been biased to preemptive gang scheduling, although non-preemptive gang scheduling (NPG) is practical for inherently non-preemptive tasks and tasks that incur large preemption over- head. In this paper, focusing on a new type of priority-inversion incurred by NPG, we design a generalized NPG framework, called NPG*, under which each task has an option to allow or dis- allow the situation that incurs the priority-inversion specialized for NPG. To demonstrate the effectiveness of NPG* in terms of timing guarantees, we target NPG*-FP by employing fixed- priority scheduling (FP) as a prioritization policy, and develop the first NPG*-FP schedulability test and its improved version under a given assignment of the allowance/disallowance option to each task. We then develop the optimal allowance/disallowance assignment algorithm, which finds an assignment (if exists) that makes a target task set schedulable by the proposed schedula- bility tests. Via simulations, we demonstrate that the assignment algorithm associated with the schedulability tests for NPG*-FP can find a number of additional schedulable task sets, each of which has not been covered by the traditional NPG framework.
Different from existing MOT (Multi-Object Tracking) techniques that usually aim at improving tracking accuracy and average FPS, real-time systems such as autonomous vehicles necessitate new requirements of MOT under limited computing resources: (R1) guarantee of timely execution and (R2) high tracking accuracy. In this paper, we propose RT-MOT, a novel system design for multiple MOT tasks, which address R1 and R2. Focusing on multiple choices of a workload pair of detection and association, which are two main components of the tracking- by-detection approach for MOT, we tailor a measure of object confidence for RT-MOT and develop how to estimate the measure for the next frame of each MOT task. By utilizing the estimation, we make it possible to predict tracking accuracy variation according to different workload pairs to be applied to the next frame of an MOT task. Next, we develop a novel confidence- aware real-time scheduling framework, which offers an offline timing guarantee for a set of MOT tasks based on non-preemptive fixed-priority scheduling with the smallest workload pair. At run-time, the framework checks the feasibility of a priority- inversion associated with a larger workload pair, which does not compromise the timing guarantee of every task, and then chooses a feasible scenario that yields the largest tracking accuracy improvement based on the proposed prediction. Our experiment results demonstrate that RT-MOT significantly improves overall tracking accuracy by up to 1.5×, compared to existing popu- lar tracking-by-detection approaches, while guaranteeing timely execution of all MOT tasks.
Mixed-criticality (MC) scheduling becomes popular in real-time systems as it supports different criticality levels in a resource-efficient manner. Although it has been well established (i) how to guarantee MC schedulability offline, existing studies have paid less attention to achieve (ii) how to minimize deadline misses of low-criticality tasks at runtime; in addition, it has not matured yet how to address (ii) without compromising (i). In this paper, we propose MC-FLEX, which employs a task-level mode transition mechanism (as opposed to system-level one). MC-FLEX not only determines time instants at which each high-criticality task enters and exits the critical mode in a task level, but also selects time instants and target low-criticality task(s) to be dropped and resumed for each task-level mode switch of individual high-criticality tasks, yielding the achievement of both (i) and (ii). Via simulation results, we demonstrate that the proposed framework reduces the job deadline miss ratio of low-criticality tasks at runtime (by over 54.8% compared to the existing work), without compromising offline MC schedulability.
ISSN: 0018-9340
As multiple software components with different safety-criticality levels are integrated on a shared computing platform, a real-time embedded system becomes a mixed-criticality (MC) system, which should provide timing guarantees at all different levels of assurance to software components with different criticality levels. In the real-time systems community, the concept of an MC system is regarded as a promising, emerging solution to solve an inherent challenge of real-time systems: pessimistic reservation of computing resources, which yields a low resource-utilization for the sake of guaranteeing timing requirements. Since a timing guarantee should be provided before a real-time system starts to operate, its feasibility has been extensively studied for single-criticality systems; however, the same cannot be said for MC systems. In this paper, we develop necessary feasibility tests for MC real-time embedded systems, which is the first study that yields non-trivial results for MC necessary feasibility on both uniprocessor and multiprocessor platforms. To this end, we investigate characteristics of MC necessary feasibility conditions, and identify new challenges posed by the characteristics. By addressing those challenges, we develop two collective necessary feasibility tests and their simplified versions, which are able to exploit a tradeoff between capability in finding infeasible task sets and time-complexity. The simulation results demonstrate that the proposed tests find a number of additional infeasible task sets for both uniprocessor and multiprocessor platforms, which have been proven neither feasible nor infeasible by any existing studies.
ISSN: 1045-9219
As real-time object detection systems, such as au- tonomous cars, need to process input images acquired from multiple cameras, they face significant challenges in delivering accurate and timely inferences often based on machine learning (ML). To meet these challenges, we want to provide different levels of object detection accuracy and timeliness to different portions within each input image with different criticality levels. Specifically, we develop DNN-SAM, a dynamic Split-And-Merge Deep Neural Network (DNN) execution and scheduling framework, that enables seamless split-and-merge DNN execution for unmodified DNN models. Instead of processing an entire input image once in a full DNN model, DNN-SAM first splits a DNN inference task into two smaller sub-tasks?a mandatory sub-task dedicated for a safety-critical (cropped) portion of each image and an optional sub-task for processing a down-scaled image?then executes them independently, and finally merges their results into a complete inference. To achieve DNN-SAM’s timely and accurate detection of objects in each image, we also develop two scheduling algorithms that prioritize sub-tasks according to their criticality levels and adaptively adjust the scale of the input image to meet the timing constraints while minimizing the response time of mandatory sub-tasks or maximizing the accuracy of optional sub-tasks. We have implemented and evaluated DNN-SAM on a representative ML framework. Our evaluation shows DNN-SAM to improve detection accuracy in the safety-critical region by 2.0?3.7× and lower average inference latency by 4.8?9.7× over existing approaches without violating any timing constraints
Deep neural networks (DNNs) have shown remarkable success in various machine-learning (ML) tasks useful for many safety-critical, real-time embedded systems. The foremost design goal for enabling DNN execution on real-time embedded systems is to provide worst-case timing guarantees with limited computing resources. Yet, the state-of-the-art ML frameworks hardly leverage heterogeneous computing resources (i.e., CPU, GPU) to improve the schedulability of real-time DNN tasks due to several factors, which include a coarse-grained resource allocation model (one-resource-per-task), the asymmetric nature of DNN execution on CPU and GPU, and lack of schedulabilityaware CPU/GPU allocation scheme. This paper presents, to the best of our knowledge, the first study of addressing the above three major barriers and examining their cooperative effect on schedulability improvement. In this paper, we propose LaLaRAND, a real-time layer-level DNN scheduling framework, that enables flexible CPU/GPU scheduling of individual DNN layers by tightly coupling CPU-friendly quantization with fine-grained CPU/GPU allocation schemes (one-resource-per-layer) while mitigating accuracy loss without compromising timing guarantees. We have implemented and evaluated LaLaRAND on top of the state-of-theart ML framework to demonstrate its effectiveness in making more DNN task sets schedulable by 56% and 80% over an existing approach and a baseline (vanilla PyTorch), respectively, with only up to -0.4% of performance (inference accuracy) difference.
As machine learning (ML) has been proven effective in solving various problems, researchers in the real-time systems (RT) community have recently paid increasing attention to ML. While most of them focused on timing issues for ML applications (i.e., RT for ML), only a little has been done on the use ML for solving fundamental RT problems. In this paper, we aim at utilizing ML to solve a fundamental RT problem of priority assignment for global fixed-priority preemptive (gFP) scheduling on a multiprocessor platform. This problem is known to be challenging in the case of a large number (n) of tasks in a task set because exhaustive testing of all priority assignments (as many as n!) is intractable and existing heuristics cannot find a schedulable priority assignment, even if exists, for a number of task sets. We systematically incorporate RT domain knowledge into ML and develop an ML framework tailored to the problem, called PAL. First, raising and addressing technical issues including neural architecture selection and training sample regulation, we enable PAL to infer a schedulable priority assignment of a set of n tasks, by training PAL with same-size (i.e., with n tasks) samples each of whose schedulable priority assignment has already been identified. Second, considering the exhaustive testing of all priority assignments of each task set with large n makes it intractable to provide training samples to PAL, we derive inductive properties that can generate training samples with large n from those with small n, through empirical observation of PAL and mathematical analysis of the target gFP schedulability test. Finally, utilizing the inductive properties and additional techniques, we propose how to systematically implement PAL whose training sample generation process not only yields unbiased samples but also is tractable even for large n. Our experimental results demonstrate PAL covers a number of additional task sets, each of which has never been proven schedulable by any existing approaches for gFP.
Although essential for inherently non-preemptive tasks and favorable to tasks with large preemption/migration overheads, non-preemptive scheduling has not been thoroughly studied compared to preemptive scheduling. In particular, existing studies for non-preemptive scheduling could not effectively exploit being non-work-conserving (i.e., idling processor(s) intentionally), failing to achieve its full schedulability capability. In this paper, we propose the first non-preemptive scheduling framework that covers work-conserving-infeasible task sets (each of which is proven unschedulable by every work-conserving non-preemptive scheduling), without knowledge of future release patterns of tasks (i.e., called clairvoyance). To this end, we first discover the following principle: without clairvoyance, it is impossible to generate a feasible schedule for work-conserving-infeasible task sets on a uniprocessor platform. To make it possible on a multiprocessor platform, we design the NWC(N)-NP-* framework that systematicaly idles up to N processors so as to enable N designated tasks (that yield work-conserving-infeasibility) to be schedulable without clairvoyance, and derive important properties of the framework. We then target the framework associated with fixedpriority scheduling (as a prioritization policy), and develop its schedulability test by utilizing the framework's properties. Our simulation results demonstrate that the proposed framework successfully covers a number of work-conserving-infeasible task sets, none of which can be deemed schedulable by any previous approach.
Modern electric systems, such as electric vehicles, mobile robots, nano satellites, and drones, require to support various power-demand operations for user applications and system maintenance. This, in turn, calls for advanced power management that jointly considers power demand by the operations and power supply from various sources, such as batteries, solar panels, and supercapacitors. In this article, we develop a power scheduling framework for a reliable energy storage system with multiple powersupply sources and multiple power-demand operations. Specifically, we develop offline power-supply guarantee analysis and online power management. The former provides an offline power-supply guarantee such that every power-demand operation completes its execution in time while the sum of power required by individual operations does not exceed the total power supplied by the entire energy storage system at any time; to this end, we develop a plain power-supply analysis as well as its improved version using real-time scheduling techniques. On the other hand, the latter efficiently utilizes the surplus power available at runtime for improving system performance; we propose two approaches, depending on whether future scheduling information of power-demanding tasks is available or not. For evaluation, we perform simulations to evaluate both the plain and improved analyses for offline power guarantee under various synthetic power-demand operations. In addition, we have built a simulation model and demonstrated that the proposed framework with the offline analysis and online management not only guarantees the required power-supply, but also enhances system performance by up to 56.49 percent.
ISSN: 1045-9219
While feasibility of timing guarantees has been extensively studied for single-criticality (SC) task systems, the same cannot be said true for mixed-criticality (MC) task systems. In particular, there exist only a few studies that address necessary feasibility conditions for MC task systems, and all of them have derived trivial results from existing SC studies that rely on simple demand-supply comparison. In this paper, we develop necessary feasibility tests for MC task systems on a uniprocessor platform, which is the first study that yields non-trivial results for MC necessary feasibility. To this end, we investigate characteristics of MC necessary feasibility conditions. Due to the existence of the mode change and consequences thereof, the characteristics pose new challenges that cannot be resolved by existing techniques for SC task systems, including how to calculate demand when the mode change occurs, how to determine the target sub-intervals for demand-supply comparison, how to derive an infeasibility condition from demand-supply comparisons with different possible mode change instants, how to select a scenario to specify the mode change instant without the target scheduling algorithm, and how to find infeasible task sets with reasonable time-complexity. By addressing those challenges, we develop a new necessary feasibility test and its simplified version. The simulation results demonstrate that the proposed tests find a number of additional infeasible task sets which have been proven neither feasible nor infeasible by any existing studies.
Battery aging is one of the critical issues in battery-powered electric systems. However, this issue has not received much attention in the real-time systems community. In this paper, we present the first attempt to translate the problem of minimizing battery aging subject to timing requirements into a real-time scheduling problem, addressing the following issues. (i) Can scheduling make a systematic impact on battery aging? If so, which scheduling principles are favorable to minimizing battery aging? (ii) If there exists any, how can we build upon the scheduling principle to guarantee real-time requirements? For (i), we first illuminate the connection between task scheduling and battery aging minimization and then derive a principle for task scheduling from abstracting the complicated dynamics of battery aging, which is to minimize the variance of total power consumption over time. In addition, we implement a battery aging simulator and use it to verify the effectiveness of the proposed principle in minimizing battery aging and its impact on quantitative improvement. For (ii), we propose a scheduling framework that separates control for timing guarantees from that for battery aging minimization. Such a separation allows reducing the complexity significantly such that we can employ existing scheduling algorithm and schedulability analysis for real-time guarantee and tailor the proposed scheduling principle to decelerate battery aging without taking real-time guarantees into accounts. Our simulation results show that the proposed framework can extend the battery lifespan by up to 144.4%.
Despite recent advances, there still remain many problems to design reliable cyber-physical systems. One of the typical problems is to achieve a seemingly conflicting goal, which is to support timely delivery of real-time flows while improving resource efficiency. Recently, the concept of mixed-criticality (MC) has been widely accepted as useful in addressing the goal for real-time resource management. However, it has not been yet studied well for real-time communication. In this paper, we present the first approach to support MC flow scheduling on switched Ethernet networks leveraging an emerging network architecture, software-defined networking (SDN). Though SDN provides flexible and programmatic ways to control packet forwarding and scheduling, it yet raises several challenges to enable real-time MC flow scheduling on SDN, including: 1) how to handle (i.e., drop or re-prioritize) out-of-mode packets in the middle of the network when the criticality mode changes and 2) how the mode change affects end-to-end transmission delays. Addressing such challenges, we develop MC-SDN that supports real-time MC flow scheduling by extending SDN-enabled switches and OpenFlow protocols. It manages and schedules MC packets in different ways depending on the system criticality mode. To this end, we carefully design the mode change protocol that provides analytic mode change delay bound, and then resolve implementation issues for system architecture. For evaluation, we implement a prototype of MC-SDN on top of Open vSwitch, and integrate it into a real world network testbed as well as a 1/10 autonomous vehicle. Our extensive evaluations with the network testbed and vehicle deployment show that MC-SDN supports MC flow scheduling with minimal delays on forwarding rule updates and it brings a significant improvement in safety in a real-world application scenario.
ISSN: 2327-4662
These days, the term of Internet of Things (IoT) becomes popular to interact and cooperate with individual smart objects, and one of the most critical challenges for IoT is to achieve efficient resource sharing as well as ensure safety-stringent timing constraints. To design such reliable real-time IoT, this paper focuses on the concept of mixed-criticality (MC) introduced to address the low processor utilization on traditional real-time systems. Although different worst-case execution time estimates depending on criticality are proven effective on processor scheduling, the MC concept is not yet mature on distributed systems (such as IoT), especially with end-to-end deadline guarantee. To the best of our knowledge, this paper presents the first attempt to apply the MC concept into interference (or jitter), which is a complicated source of pessimism when analyzing the schedulability of distributed systems. Our goal is to guarantee the end-to-end deadlines of high-criticality flows and minimize the deadline miss ratio of low-criticality flows in distributed systems. To achieve this goal, we introduce a jitter-based MC (JMC) scheduling framework, which supports node-level mode changes in distributed systems. We present an optimal feasibility condition (subject to given schedulability analysis) and two policies to determine jitter-threshold values to achieve the goal in different conditions. Via simulation results for randomly generated workloads, JMC outperforms an existing criticality-monotonic scheme in terms of achieving higher schedulability and fewer deadline misses.
ISSN: 2327-4662
Real-time scheduling is the primary research for designing real-time systems whose correctness is determined by not only logical correctness but also timely execution. Real-time scheduling involves two fundamental issues: scheduling algorithm design and schedulability analysis development, which aim at developing a prioritization policy for real-time tasks and offering their timing guarantees at design time, respectively. Among the numerous scheduling algorithms and schedulability analysis for a multiprocessor platform, the contention-free (CF) policy and response-time analysis (RTA) have received considerable attention owing to their wide applicability and high analytical performance, respectively. Notwithstanding their effectiveness, it has been conjectured that it is not feasible to exploit the two techniques together. In this study, we propose a new schedulability analysis for the CF policy, referred to as pseudo-response time analysis (PRTA), which exploits a new notion of pseudo-response time effectively capturing the time instant at which the schedulability of a task is guaranteed under the CF policy. To demonstrate the effectiveness of PRTA, we apply PRTA to the existing earliest deadline first and rate monotonic scheduling algorithms employing the CF policy, and show that up to 46.4% and 18.3% schedulability performance improvement can be achieved, respectively, compared to those applying the existing schedulability analysis.
ISSN: 0164-1212
Since most modern mobile and electric vehicles are powered by Lithium-ion batteries, they need advanced power management that jointly considers battery performance and related electrochemical reactions. To meet these needs, we develop a charging diagnosis and prognosis system for effective battery control. We first examine the battery model that can capture electrochemical reactions and battery performance. Based on this model, we develop a charging diagnosis system that determines battery internal states and predicts their trend over operational cycles in various environments. Next, we propose a prognosis system to estimate battery's end-of-life (EOL) and determine optimal operating environments. Our in-depth experiments demonstrate that the proposed diagnosis and prognosis system estimates battery parameters and their degradation over operational cycles in various environments with high accuracy and without degrading user-perceived experience.
The development of complex cyber-physical systems necessitates real-time networking with timing guarantees even in the presence of a link fault. Targeting firm real-time flows with the maximum allowable number of continuous deadline misses, this paper introduces FR-SDN, a fault-resilient SDN (Software-Defined Networking) framework that satisfies the timing requirements of firm real-time flows. To this end, we first investigate individual steps for path restoration: fault recognition, path recalculation, and path reassignment. We then design novel system architecture that reduces the delay of the fault recognition and path reassignment steps to potentially assign more time budget to the path recalculation step. Based on the calculation of tight upper-bounds on the delays in individual steps under the proposed system design, we derive a necessary feasibility condition that guarantees the timing requirements of firm real-time flows, and we calculate a time budget for the path recalculation step. Finally, we develop a multi-constrained path finding algorithm that can dynamically adjust the scope of flows to reroute according to the time budget. To the best of our knowledge, FR-SDN is the first study on adaptive path restoration for real-time flows, taking into account path restoration delay and fault tolerance constraints in case of link fault. We have implemented and evaluated FR-SDN on top of Open vSwitch to demonstrate its effectiveness, achieving an order of magnitude reduction in path restoration delay. In addition, we have deployed FR-SDN into a 1/10 scale autonomous vehicle and have shown, via an in-depth case study of adaptive cruise control, that FR-SDN is able to meet all fault tolerance requirements so that it can behave similarly as if there were no link failure.
In this paper, we present the first approach to support mixed-criticality (MC) flow scheduling on switched Ethernet networks leveraging an emerging network architecture, Software-Defined Networking (SDN). Though SDN provides flexible and programmatic ways to control packet forwarding and scheduling, it yet raises several challenges to enable real-time MC flow scheduling on SDN, including i) how to handle (i.e., drop or reprioritize) out-of-mode packets in the middle of the network when the criticality mode changes, and ii) how the mode change affects end-to-end transmission delays. Addressing such challenges, we develop MC-SDN that supports real-time MC flow scheduling by extending SDN-enabled switches and OpenFlow protocols. It manages and schedules MC packets in different ways depending on the system criticality mode. To this end, we carefully design the mode change protocol that provides analytic mode change delay bound, and then resolve implementation issues for system architecture. For evaluation, we implement a prototype of MC-SDN on top of Open vSwitch, and integrate it into a real world network testbed as well as a 1/10 autonomous vehicle. Our extensive evaluations with the network testbed and vehicle deployment show that MC-SDN supports MC flow scheduling with minimal delays on forwarding rule updates and it brings a significant improvement in safety in a real-world application scenario.
Real-time scheduling for Mixed-Criticality (MC) systems has received a growing attention as real-time embedded systems accommodate various tasks with different levels of criticality. While many studies have addressed how to guarantee timing requirements for MC systems with uniprocessor and multiprocessors, most of them have focused on supporting preemptive tasks. On the other hand, there have been few studies to address non-preemptive scheduling especially for MC multiprocessor platforms, in which the jobs under execution cannot be preempted by other jobs. In this paper, we develop schedulability tests for non-preemptive scheduling, which is the first attempt for MC multiprocessor systems. To this end, we first generalize an existing NP-EDF (Non-Preemptive Earliest Deadline First) schedulability test developed for single-criticality multiprocessor systems, towards for MC multiprocessor systems. For the generalization, we introduce new timing guarantee techniques for the system transition between two different criticalities, which is one of the key features in MC systems. We next extend the proposed NP-EDF schedulability test towards NP-EDFVD (NP-EDF with Virtual Deadlines) that is specialized for MC systems, and pose a virtual deadline assignment problem. We develop an optimal virtual deadline assignment policy using a control knob of the system-level deadline-reduction parameter and then a suboptimal one for the task-level parameter. Our simulation results demonstrate that the NP-EDFVD schedulability test with the proposed virtual deadline assignment policies finds a number of additional schedulable task sets, which are not schedulable by the NP-EDF schedulability test.
ISSN: 1045-9219
A cyber-physical system (CPS) usually contains multiple control loops, each responsible for controlling different physical subprocesses, that run simultaneously upon a shared platform. The foremost design goal for CPSes is to guarantee system stability and control quality with limited cyber resources. We show, via an in-depth case study, that two inter-related design parameters - sampling period and consecutive control update misses - play a key role in determining stability and control performance. However, most CPS designs, such as control-schedule co-design and fault-tolerant scheduling, focus on either sampling period or control update misses alone, but not both. To remedy this problem, we propose a new CPS task model that captures both system stability and control performance in terms of sampling period and maximum allowable number of consecutive control update misses. To demonstrate the utility and power of this model, we develop two new scheduling mechanisms, offline parameter assignment and online state-aware scheduling. The former determines the sampling period and the maximum allowable number of consecutive job deadline misses for each task while preserving system stability. The latter then generates a schedule by exploiting the state of each physical subprocess to manage job deadline misses so as to improve the overall system performance without compromising system stability. Our in-depth evaluation results demonstrate that the proposed task model and the corresponding scheduling algorithm not only enable the efficient use of computing resource, but also significantly improve control performance without compromising system stability.
Safety-critical cyber-physical systems like autonomous cars require not only different levels of assurance, but also close interactions with dynamically-changing physical environments. While the former has been studied extensively by exploiting the notion of mixed-criticality (MC) systems, the latter has not, especially in conjunction with MC systems. To fill this important gap, we conduct an in-depth case study, demonstrating the importance of capturing current physical states, and introduce the problem of achieving efficient utilization of computing resources under varying physical states in MC systems. To solve this problem, we first develop a physical-state-aware MC task model, which is a generalization of the existing basic MC task model. We then propose new slack concepts tailored to the new task model, which enable efficient utilization of computing resources for MC systems. Finally, we develop a physical-state-aware dynamic slack management framework and demonstrate how to utilize the new MC task model and slack concepts towards efficient system utilization. We show, via a case study and in-depth evaluation, that the proposed framework makes 20x less low-criticality jobs dropped over a popular MC scheduling algorithm without compromising the MC-schedulability requirements.
The contention-free policy has received attention in real-time multiprocessor scheduling owing to its wide applicability and significant improvement in offline schedulability guarantees. Utilizing the notion of contention-free slots in which the number of active jobs is smaller than or equal to the number of processors, the policy improves the schedulability by offloading executions in contending time slots to contention-free ones. In this paper, we propose the multi-level contention-free policy by exploiting a new, generalized notion of multi-level contention-free slots. In a case study, we present how the multi-level contention-free policy is applied to EDF (Earliest Deadline First) scheduling and develop a schedulability test for EDF that adopts the new policy. Our evaluation results demonstrate that the multi-level contention-free policy significantly improves the schedulability by up to 4188% and 127%, compared to vanilla EDF and EDF adopting the existing contention-free policy, respectively.
ISSN: 0164-1212
In the real-time systems community, many studies have addressed how to efficiently utilize a multiprocessor platform so as to accommodate as many periodic/sporadic real-time tasks as possible without violating any timing constraints. The scheduling theory has sufficiently matured for a set of implicit-deadline tasks (the relative deadline equal to the period), yielding a class of optimal scheduling algorithms. However, the same does not hold for a set of constrained-deadline tasks (the relative deadline no larger than the period) in that those task sets have been fully covered by neither existing implicit-deadline optimal scheduling algorithms nor heuristic scheduling algorithms., In this paper, we propose a scheduling framework that not only takes advantage of both existing implicit-deadline optimal and heuristic algorithms, but also surpasses both in finding schedulable constrained-deadline task sets. The proposed framework logically divides a given task set into the higher- and lower-priority classes and schedules the classes using an implicit-deadline optimal algorithm and a heuristic algorithm, respectively. Then, while the proposed framework guarantees schedulability of tasks in the higher-priority class by the target implicit-deadline optimal algorithm, we need to address the following technical issues for enabling tasks in the lower-priority class to efficiently reclaim remaining processor capacity while guaranteeing their schedulability: (i) division of a given task set into the two classes, (ii) selection/development of scheduling algorithms for the two classes, and (iii) development of a schedulability test for the framework with given (i) and (ii). We present a general case showing how to address (i)-(iii), and then a specific case addressing how to further improve schedulability by utilizing characteristics of the specific case. Our simulation results demonstrate that the proposed framework outperforms all existing scheduling algorithms in covering schedulable tasksets; in particular, if we focus on task sets with the system densitylarger than the number of processors, the framework finds up to446.3% additional schedulable task sets, compared to task setscovered by at least one of existing scheduling algorithms.
A time instant is said to be a critical instant for a task, if the task's arrival at the instant makes the duration between the task's arrival and completion the longest. Critical instants for a task, once revealed, make it possible to check the task's schedulability by investigating situations associated with the critical instants. This potentially results in efficient and tight schedulability tests, which is important in real-time systems. For example, existing studies have discovered critical instants under preemptive fixed-priority scheduling (P-FP), which limit interference from carry-in jobs, yielding the state-of-the-art schedulability tests on both uniprocessor and multiprocessor platforms. However, studies on schedulability tests associated with critical instants have not matured yet for non-preemptive scheduling, especially on a multiprocessor platform. In this paper, we find necessary conditions for critical instants for non-preemptive global fixed-priority scheduling (NP-FP) on a multiprocessor platform, and develop a new schedulability test that takes advantage of the finding for reducing carry-in jobs' interference. Evaluation results show that the proposed schedulability test finds up to 14.3 percent additional task sets schedulable by NP-FP, which are not deemed schedulable by the state-of-theart NP-FP schedulability test.
ISSN: 0018-9340
With the widespread adoption of multi-core architectures, it is becoming more important to develop software in ways that takes advantage of such parallel architectures. This particularly entails a shift in programming paradigms towards fine-grained, thread-parallel computing. Many parallel programming models have been introduced for targeting such intra-task thread-level parallelism. However, most successful results on traditional multi-core real-time scheduling are focused on sequential programming models. For example, thread-level parallelism is not properly captured into the concept of interference, which is key to many schedulability analysis techniques. Thereby, most interference-based analysis techniques are not directly applicable to parallel programming models. Motivated by this, we extend the notion of interference to capture thread-level parallelism more accurately. We then leverage the proposed notion of parallelism-aware interference to derive efficient EDF schedulability tests that are directly applicable to parallel task models, including DAG models, on multi-core platforms, without knowing an optimal schedule. Our evaluation results indicate that the proposed analysis significantly advances the state-of-the-art in global EDF schedulability analysis for parallel tasks. In particular, we identify that our proposed schedulability tests are adaptive to different degrees of thread-level parallelism and scalable to the number of processors, resulting in substantial improvement of schedulability for parallel tasks on multi-core platforms.
ISSN: 1045-9219
In a typical cyber-physical system (CPS), the cyber/computation subsystem controls the physical subsystem, and therefore the computer society has recently paid considerable attention to CPS research. To keep such a CPS stable, feedback control with periodic computation tasks has been widely used, and its theoretical guarantee of stability has been made with periodic real-time task models that enforce strict periodic control updates. However, some control update misses are usually allowed (e.g., via system over-design) in certain physical subsystem states (PSSes) without causing system instability, and the resources required for strict periodic control updates can thus be reduced or used for other purposes, achieving efficient controls for the entire CPS in terms of the operational cost, such as fuel consumption or tracking accuracy. In this paper, we propose a new periodic, fault-tolerant CPS task model, which not only expresses efficiency and stability of the underlying physical subsystem, but also generalizes existing periodic real-time task models, by capturing a tolerable number of control update misses in different PSSes. To demonstrate the utility of this model, we develop a new scheduling mechanism that prioritizes jobs (i.e., periodic invocations) of a set of tasks not only by the nature of each task, but also by the number of consecutive prior job deadline misses. Based on its analysis in terms of stability and efficiency, we also propose a priority-assignment policy that lowers the system operation cost without compromising stability. Our in-depth analysis and simulation results show that the scheduling mechanism and its analysis, as well as the priority-assignment policy under the proposed model not only generalize the existing periodic real-time task models, but also significantly lower the system operation cost without losing stability.
ISSN: 0164-1212
In a typical cyber-physical system (CPS), the cyber/computation subsystem controls the physical subsystem, and therefore the computer society has recently paid considerable attention to CPS research. To keep such a CPS stable, feedback control with periodic computation tasks has been widely used, and its theoretical guarantee of stability has been made with periodic real-time task models that enforce strict periodic control updates. However, some control update misses are usually allowed (e.g., via system over-design) in certain physical subsystem states (PSSes) without causing system instability, and the resources required for strict periodic control updates can thus be reduced or used for other purposes, achieving efficient controls for the entire CPS in terms of the operational cost, such as fuel consumption or tracking accuracy. In this paper, we propose a new periodic, fault-tolerant CPS task model, which not only expresses efficiency and stability of the underlying physical subsystem, but also generalizes existing periodic real-time task models, by capturing a tolerable number of control update misses in different PSSes. To demonstrate the utility of this model, we develop a new scheduling mechanism that prioritizes jobs (i.e., periodic invocations) of a set of tasks not only by the nature of each task, but also by the number of consecutive prior job deadline misses. Based on its analysis in terms of stability and efficiency, we also propose a priority-assignment policy that lowers the system operation cost without compromising stability. Our in-depth analysis and simulation results show that the scheduling mechanism and its analysis, as well as the priority-assignment policy under the proposed model not only generalize the existing periodic real-time task models, but also significantly lower the system operation cost without losing stability.
ISSN: 1045-9219
In a typical cyber-physical system (CPS), the cyber/computation subsystem controls the physical subsystem, and therefore the computer society has recently paid considerable attention to CPS research. To keep such a CPS stable, feedback control with periodic computation tasks has been widely used, and its theoretical guarantee of stability has been made with periodic real-time task models that enforce strict periodic control updates. However, some control update misses are usually allowed (e.g., via system over-design) in certain physical subsystem states (PSSes) without causing system instability, and the resources required for strict periodic control updates can thus be reduced or used for other purposes, achieving efficient controls for the entire CPS in terms of the operational cost, such as fuel consumption or tracking accuracy. In this paper, we propose a new periodic, fault-tolerant CPS task model, which not only expresses efficiency and stability of the underlying physical subsystem, but also generalizes existing periodic real-time task models, by capturing a tolerable number of control update misses in different PSSes. To demonstrate the utility of this model, we develop a new scheduling mechanism that prioritizes jobs (i.e., periodic invocations) of a set of tasks not only by the nature of each task, but also by the number of consecutive prior job deadline misses. Based on its analysis in terms of stability and efficiency, we also propose a priority-assignment policy that lowers the system operation cost without compromising stability. Our in-depth analysis and simulation results show that the scheduling mechanism and its analysis, as well as the priority-assignment policy under the proposed model not only generalize the existing periodic real-time task models, but also significantly lower the system operation cost without losing stability.
The advent of multi- and many-core processors offers enormous performance potential for parallel tasks that exhibit sufficient intra-task thread-level parallelism. With a growth of novel parallel programming models (e.g., OpenMP, MapReduce), scheduling parallel tasks in the real-time context has received an increasing attention in the recent past. While most studies focused on schedulability analysis under some well-known scheduling algorithms designed for sequential tasks, little work has been introduced to design new scheduling policies that accommodate the features of parallel tasks, such as their multi-threaded structure. Motivated by this, we refine real-time scheduling algorithm categories according to the basic unit of scheduling and propose a new priority assignment method for global task-wide thread-level fixed-priority scheduling of parallel task systems. Our evaluation results show that a finer-grained, thread-level fixed-priority assignment, when properly assigned, significantly improves schedulability, compared to a coarser-grained, task-level assignment.
ISSN: 0164-1212
Motivated by the cutting-edge two-type heterogeneous multicore chips, such as ARM's big.LITTLE, that offer a practical support for migration, this paper studies the global (or fully-migrative) approach to two-type heterogeneous multicore scheduling. Our goal is to design an optimal fully-migrative scheduling framework. To achieve this goal in an efficient and simple manner, we break the scheduling problem into two subproblems: workload assignment and schedule generation. We propose a per-cluster workload assignment algorithm, called Hetero-Split, that determines the fractions of workload of each task to be assigned to both clusters without losing feasibility with the complexity of O(n log n), where n is the number of tasks. Furthermore, it provides a couple of important properties (e.g., a dual property) that help to generate an optimal schedule efficiently. We also derive scheduling guidelines to design optimal schedulers for two-type heterogeneous multicore platforms, called Hetero-Fair. By tightly coupling the solutions of Hetero-Split and Hetero-Fair, we develop the first optimal two-type heterogeneous multicore scheduling algorithm, called Hetero-Wrap, that has the same complexity (O(n)) as in the identical multicore case. Finally, concerning a practical point of view, we derive the first bounds on the numbers of intra-and inter-cluster migrations under two-type heterogeneous multicore scheduling, respectively.
Modern electric vehicles are equipped with an advanced battery management system, responsible for providing the necessary power efficiently from batteries to electric motors while maintaining the batteries within an operational condition. Because discharge-rate and temperature of batteries affect their health and efficiency significantly, batteries are managed to mitigate their discharge and thermal stresses. In this paper, we develop a real-time, efficient integrated management system for discharge-rate and temperature of batteries. To achieve this objective, we first construct a prognosis system predicting the likely states of batteries' capacity and capability. Based on prognostic estimates of the impact of temperature and discharge-rate on the performance, we solve an optimization problem to search for efficient discharging and cooling scheduling. Our experimentation and simulation demonstrate that the proposed management enhances system performance up to 85.3%.
With increasing popularity and deployment of multi-core chips in embedded systems, a number of real-time multiprocessor scheduling algorithms have been proposed along with their schedulability analyses (or tests), which verify temporal correctness under a specific algorithm. Each of these algorithms often comes with several different schedulability tests, especially when it is difficult to find exact schedulability tests for the algorithm. Such tests usually find different task sets deemed schedulable even under the same scheduling algorithm. While these different tests have been compared with each other in terms of schedulability performance, little has been done on how to combine such different tests to improve the overall schedulability of a given scheduling algorithm beyond a simple union of their individual schedulability. Motivated by this, we propose a composition theory for schedulability tests with two new methods. The first method composes task-level timing guarantees derived from different schedulability tests, and the second one derives system-level schedulability results from a single schedulability test. The unified composition theory with these two methods then utilizes existing schedulability tests effectively so as to cover additional schedulable task sets. The proposed composition theory is shown to be applicable to most existing preemptive/non-preemptive scheduling algorithms. We also present three case-studies, demonstrating how and by how much the theory can improve schedulability by composing existing schedulability tests. Our evaluation results also show that the composition theory makes it possible to cover up to 181.7 percent additional schedulable task sets for preemptive fpEDF, preemptive EDF and non-preemptive EDF scheduling algorithms beyond their existing tests.
ISSN: 0018-9340
Recent trends toward multi-core architectures in real-time embedded systems pose challenges in designing efficient real-time multiprocessor scheduling algorithms. We believe that it is important to take into consideration both timing constraints of tasks (urgency) and parallelism restrictions of multiprocessor platforms (parallelism) together when designing scheduling algorithms. Motivated by this, we define the quasi-deadline of a job as a weighted sum of its absolute deadline (capturing urgency) and its worst case execution time (capturing parallelism) with a system-level control knob to balance urgency and parallelism effectively. Using the quasi-deadline to prioritize jobs, we propose two new scheduling algorithms, called EQDF (earliest quasi-deadline first) and EQDZL (earliest quasi-deadline until zero laxity), that are categorized into job-level fixed-priority (JFP) scheduling and job-level dynamic-priority (JDP) scheduling, respectively. This paper provides a new schedulability analysis for EQDF/EQDZL scheduling and addresses the problem of priority assignment under EQDF/EQDZL by determining a right value of the system-level control knob. It presents optimal and heuristic solutions to the problem subject to our proposed EQDF and EQDZL analysis. Our simulation results show that EQDF and EQDZL can improve schedulability significantly compared to EDF and EDZL, respectively.
ISSN: 0164-1212
For timing guarantees of a set of real-time tasks under a target scheduling algorithm, a number of schedulability tests have been studied. However, there still exist many task sets that are potentially schedulable by a target scheduling algorithm, but proven schedulable by none of existing schedulability tests, especially on a multiprocessor platform. In this paper, we propose a new notion of time-reversibility of schedulability tests, which yields tighter schedulability guarantees by viewing real-time scheduling under a change in the sign of time. To this end, we first define the notion of a time-reversed scheduling algorithm against a target scheduling algorithm, for example, the time-reversed scheduling algorithm against EDF (Earliest Deadline First) is LCFS (Last-Come, First-Served), and the converse also holds. Then, a schedulability test for a scheduling algorithm is said to be time-reversible with respect to schedulability, if all task sets deemed schedulable by the test are also schedulable by its time-reversed scheduling algorithm. To exploit the notion of time-reversibility for tighter schedulability guarantees, we not only prove time-reversibility of an existing schedulability test, but also develop a new time-reversible schedulability test, both of which cover additional schedulable task sets. Next, we generalize the time-reversibility theory towards partial execution. Utilizing the notion, we can assure the schedulability of a task under a target scheduling algorithm in a divide-and-conquer manner: (i) the first some units of execution guaranteed by a schedulability test for the scheduling algorithm, and (ii) the remaining execution guaranteed by a time-reversible (with respect to partial execution) schedulability test for its time-reversed scheduling algorithm. Such a divide-and-conquer approach has not been directly applied to existing schedulability tests in that they cannot address (ii) effectively. As a case study,this paper develops RTA (Response-Time Analysis) for LCFS,proves its time-reversibility, and applies the divide-and-conquerapproach to the test along with an existing EDF schedulabilitytest. Our simulation results show that the time-reversibility theoryhelps to find up to 13.1% additional EDF-schedulable task setson a multiprocessor platform.
Electric vehicles (EVs) are equipped with a large number of expensive battery cells, necessitating an effective battery management system (BMS) which protects the battery cells from harsh conditions while providing the required power efficiently. The discharge/charge rate affects battery health significantly, and existing BMSes employ simple discharge/charge rate scheduling so as to prevent weak cells from excessive discharge/charge. In this paper, we design and evaluate the real-time management of battery discharge/charge rate to extend battery life for EVs based on the physical dynamics and operation history of batteries. We first explore a modern energy storage system for EVs to capture physical dynamics and their impact on the battery discharge/charge rate, for example, a regenerative braking system for reusing the dissipated energy leads to current surges into the batteries, which shortens battery life. Based on understanding of the effects of discharge/charge rate in an energy storage system, we devise control knobs for manipulating the rate. Then, we design an adaptive discharge/charge rate management algorithm that determines the control knobs with a reconfigurable energy storage architecture. Our in-depth evaluation results demonstrate that the proposed discharge/charge rate management improves battery life up to 37.7% at little additional cost over the existing energy storage systems.
The earliest-deadline-first (EDF) policy has been widely studied for the scheduling of real-time jobs for its effectiveness and simplicity. However, since each preemption incurs an additional delay to the execution of jobs, the effectiveness of EDF is affected greatly by the underlying preemption policy that determines if and when a higher-priority job is allowed to preempt a currently executing lower-priority job. To address this problem, we propose a new and better (in meeting job deadlines) preemption policy of EDF, given a non-zero preemption delay. Specifically, we propose a controlled preemption (CP) policy that controls the condition of preempting jobs, whereas existing approaches focus on that of preempted jobs. We define cp-EDF in which the CP policy is applied to EDF, and analyze its schedulability. This schedulability analysis is then utilized to develop an algorithm that assigns the optimal control parameters of cp-EDF. Our in-depth evaluation has demonstrated that cp-EDF with the optimal parameter assignment improves EDF schedulability over existing preemption policies by up to 7.4%.
ISSN: 0018-9340
While tasks may be preemptive or non-preemptive (due to their transactional operations), deadline guarantees in multi-core systems have been made only for those task sets in each of which all tasks are preemptive or non-preemptive, not a mixture thereof,i.e., fully preemptive or fully non-preemptive. In this paper, we first develop a schedulability analysis framework that guarantees the timing requirements of a given task set in which a task can be either preemptive or non-preemptive in multi-core systems. We then apply this framework to the prioritization polices of EDF (earliest deadline first) and FP (fixed priority), yielding schedulability tests of mpn-EDF (Mixed Preemptive/Non-preemptive EDF) and mpn-FP, which are generalizations of corresponding fully-preemptive and non-preemptive algorithms, i.e., fp-EDF and np-EDF, and fp-FP and np-FP. In addition to their timing guarantees for any task set that consists of a mixture of preemptive and non-preemptive tasks, the tests outperform the existing schedulability tests of np-EDF andnp-FP (i.e., special cases of mpn-EDF and mpn-FP). Using these tests, we also improve schedulability by developing an algorithm that optimally disallows preemption of a preemptive task under a certain assumption. We demonstrate via simulation that the algorithm finds up to 47.6 percent additional task sets that are schedulable with mpn-FP (likewise mpn-EDF), but not with fp-FP and np-FP (likewisefp-EDF and np-EDF).
ISSN: 1045-9219
The potential of multi-core chips for high performance and reliability at low cost has made them ideal computing platforms for embedded real-time systems. As a result, power management of a multi-core chip has become an important issue in the design of embedded real-time systems. Most existing approaches have been designed to regulate the behavior of average power consumption, such as minimizing the total energy consumption or the chip temperature. However, little attention has been paid to the worst-case behavior of instantaneous power consumption on a chip, called chip-level peak power consumption, an important design parameter that determines the cost and/or size of chip design/packaging and the underlying power supply. We address this problem by reducing the chip-level peak power consumption at design time without violating any real-time constraints. We achieve this by carefully scheduling real-time tasks, without relying on any additional hardware implementation for power management, such as dynamic voltage and frequency scaling. Specifically, we propose a new scheduling algorithm FP Θ that restricts the concurrent execution of tasks assigned on different cores, and perform its schedulability analysis. Using this analysis, we develop a method that finds a set of concurrent executable tasks, such that the design-time chip-level peak power consumption is minimized and all timing requirements are met. We demonstrate via simulation that the proposed method not only keeps the design-time chip-level peak power consumption as low as the theoretical lower bound for trivial cases, but also reduces the peak power consumption for non-trivial cases by up to 12.9 percent compared to the case of no restriction on concurrent task execution.
ISSN: 1045-9219
Electric vehicles (EVs) are powered by a large number of battery cells, requiring an effective battery management system (BMS) to maintain the battery cells in an operational condition while providing the necessary power efficiently. Temperature is one of the most important factors for battery operation, and existing BMSes have thus employed simple thermal management policies so as to prevent battery cells from very high and low temperatures which may likely cause their explosion and malfunction, respectively. In this paper, we study thermo-physical characteristics of battery cells, and design a battery thermal management system that achieves efficiency and reliability by active thermal controls. We first analyze the effect of a temperature change on the basic operation of a battery cell. We then show how it can cause thermal and general problems, e.g., a thermal runaway that results in explosion of battery cells, and unbalanced state of charge (SoC) that degrades battery cells' performance. Based on this understanding of thermal behavior, we finally develop temperature-control approaches which are then used to design a battery thermal management system. Our simulation results demonstrate that the proposed thermal management system improves battery performance by up to 58.4% without compromising reliability over the existing simple thermal management.
In real-time systems, schedulability analysis has been widely studied to provide offline guarantees on temporal correctness, producing many analysis methods. The demand-based schedulability analysis method has a great potential for high schedulability performance and broad applicability. However, such a potential is not yet fully realized for real-time multi-core scheduling mainly due to (i) the difficulty of calculating the resource demand under dynamic priority scheduling algorithms that are favorable to multi-cores, and (ii) the lack of understanding how to combine the analysis framework with deadline-miss conditions specialized for those scheduling algorithms. Addressing those two issues, to the best of our knowledge, this paper presents the first demand-based schedulability analysis for dynamic job-priority scheduling algorithms: EDZL (Earliest Deadline first until Zero-Laxity) and LLF (Least Laxity First), which are known to be effective for real-time multi-core scheduling. To this end, we first derive demand bound functions that compute the maximum possible amount of resource demand of jobs of each task while the priority of each job can change dynamically under EDZL and LLF. Then, we develop demand-based schedulability analyses for EDZL and LLF, by incorporating those new demand bound functions into the existing demand-based analysis framework. Finally, we combine the framework with additional deadline-miss conditions specialized for those two laxity-based dynamic job-priority scheduling algorithms, yielding tighter schedulability analyses. Via simulations, we demonstrate that the proposed schedulability analyses outperform the existing schedulability analyses for EDZL and LLF.
ISSN: 0164-1212
A time slot is defined as contention-free if the number of jobs with remaining executions in the slot is no larger than the number of processors, or contending, otherwise. Then an important property holds that in any contention-free slot, all jobs with remaining executions are guaranteed to be scheduled as long as the scheduler is work-conserving. This article aims at improving schedulability by utilizing the contention-free slots. To achieve this, this article presents a policy (called CF policy) that moves some job executions from contending slots to contention-free ones. This policy can be employed by any work-conserving, preemptive scheduling algorithm, and we show that any algorithm extended with this policy dominates the original algorithm in terms of schedulability. We also present improved schedulability tests for algorithms that employ this policy, based on the observation that interference from jobs is reduced when their executions are postponed to contention-free slots. Simulation results demonstrate that the CF policy, incorporated into existing algorithms, significantly improves schedulability of those existing algorithms.
ISSN: 1539-9087
Satellites are indispensable for broadcast, weather forecast, navigation, and many other applications, but their design entails a number of stringent requirements, such as limited space and weight, impossible/costly online repairs, severe radiation, and a wide range of temperature they have to withstand. These requirements can only be met by an effective, robust co-design of physical and computing (control) parts of each satellite, making them prototypical cyber-physical systems (CPSes). Of the various CPS issues related to satellites, this paper focuses on offline design and online management of satellite power systems. Specifically, we analyze and model unique characteristics of power supply and demand of a satellite, which are dictated by the periodicity of power generation from solar panels and the nonlinear behavior of rechargeable battery cells. Based on the understanding of these characteristics, we first propose how to find the best configuration (e.g., the number, the arrangement, and the type) of solar panels and battery cells at design time, such that all tasks can be executed without power shortage throughout the satellite's mission lifetime. Second, we propose how to manage power online so as to execute the highest QoS versions of tasks (thus yielding the most power-effective performance) without compromising the power-sufficiency guarantee under a given configuration. As a case study, we study cubic-shaped nanosatellites, which have been launched multiple times since 2004. We borrow their architecture, configuration and parameters, and demonstrate the effectiveness of our design and management of satellite power systems.
To enable real-time systems to adapt to dynamically changing environments, update functionalities and/or accommodate those tasks migrated from other failed sub-systems, there have been a number of studies on making timing guarantees while accounting for change of parameters and addition/deletion of tasks. While most of them have dealt with "transition" protocols that delay next task releases or discard the unfinished tasks released before the transition, such protocols are not suitable for many control systems in which missing/delaying control updates (by completing periodic tasks) even during a transition or mode-change may cause system instability or incur a significant incremental operational cost. In this paper, we focus on a transition protocol that does not miss/delay control updates during a system transition, and develop a new schedulability analysis for the transition in a real-time multi-core system, which provides sufficient timing guarantees without requiring any online information, such as the release and execution patterns of tasks and the start time of a transition. To achieve this, we extend an existing popular schedulability analysis framework for nontransitional tasks, and identify the scenarios that maximize the duration of a task's interference to another task in the case of a transition. Since the analysis works for any arbitrary transition order of tasks, we can improve the schedulability performance by enforcing a specific order. We formulate the problem of assigning an optimal transition order, and develop a solution by deriving some properties of optimality. Our evaluation results demonstrate that the proposed solution finds more schedulable task sets, which are not covered by naive approaches.
We present a novel, linear programming (LP)-based scheduling algorithm that exploits heterogeneous multicore architectures such as CPUs and GPUs to accelerate a wide variety of proximity queries. To represent complicated performance relationships between heterogeneous architectures and different computations of proximity queries, we propose a simple, yet accurate model that measures the expected running time of these computations. Based on this model, we formulate an optimization problem that minimizes the largest time spent on computing resources, and propose a novel, iterative LP-based scheduling algorithm. Since our method is general, we are able to apply our method into various proximity queries used in five different applications that have different characteristics. Our method achieves an order of magnitude performance improvement by using four different GPUs and two hexa-core CPUs over using a hexa-core CPU only. Unlike prior scheduling methods, our method continually improves the performance, as we add more computing resources. Also, our method achieves much higher performance improvement compared with prior methods as heterogeneity of computing resources is increased. Moreover, for one of tested applications, our method achieves even higher performance than a prior parallel method optimized manually for the application. We also show that our method provides results that are close (e.g., 75 percent) to the performance provided by a conservative upper bound of the ideal throughput. These results demonstrate the efficiency and robustness of our algorithm that have not been achieved by prior methods. In addition, we integrate one of our contributions with a work stealing method. Our version of the work stealing method achieves 18 percent performance improvement on average over the original work stealing method. This result shows wide applicability of our approach.
ISSN: 1077-2626
In real-time systems, correctness depends not only on functionality but also on timeliness. A great number of scheduling theories have been developed for verification of the temporal correctness of jobs (software) in such systems. Among them, the Earliest Deadline first until Zero-Laxity (EDZL) scheduling algorithm has received growing attention thanks to its effectiveness in multicore real-time scheduling. However, the true potential of EDZL has not yet been fully exploited in its schedulability analysis as the state-of-the-art EDZL analysis techniques involve considerable pessimism. In this paper, we propose a new EDZL multicore schedulability test. We first introduce an interesting observation that suggests an insight toward pessimism reduction in the schedulability analysis of EDZL. We then incorporate it into a well-known existing Earliest Deadline First (EDF) schedulability test, resulting in a new EDZL schedulability test. We demonstrate that the proposed EDZL test not only has lower time complexity than existing EDZL schedulability tests, but also significantly improves the schedulability of EDZL by up to 36.6 percent compared to the best existing EDZL schedulability tests.
ISSN: 0098-5589
The trend towards multi-core/many-core architectures is well underway. It is therefore becoming very important to develop software in ways that take advantage of such parallel architectures. This particularly entails a shift in programming paradigms towards fine-grained, thread-parallel computing. Many parallel programming models have been introduced targeting such intra-task thread-level parallelism. However, most successful results on traditional multi-core real-time scheduling are focused on sequential programming models. For example, thread-level parallelism is not properly captured into the concept of interference, which is key to many schedulability analysis techniques. Thereby, most interference-based analysis techniques are not directly applicable to parallel programming models. Motivated by this, we extend the notion of interference to capture thread-level parallelism more accurately. We then leverage the proposed notion of parallelism-aware interference to derive efficient EDF schedulability tests that are directly applicable to synchronous parallel task models on multi-core platforms. Our evaluation results indicate that the proposed analysis significantly advances the state-of-the-art in EDF schedulability analysis for synchronous parallel tasks.
A battery management system (BMS) is responsible for protecting the battery from damage, predicting battery life, and maintaining the battery in an operational condition. In this paper, we propose an efficient way of predicting the power requirements of electric vehicles (EVs) based on a history of their power consumption, speed, and acceleration, as well as the road information from a pre-downloaded map. The predicted power requirement is then used by the BMS to prevent the damage of battery cells that might result from high discharge rates. This prediction also helps BMS efficiently schedule and allocate battery cells in real time to meet an EV's power demands. For accurate prediction of power requirements, we need an accurate model for the power requirement of each given application. We generate this model in real time by collecting and using historical data of power consumption, speed, acceleration, and road information such as slope and speed limit. By using this information and the operator's driving pattern, the model extracts the vehicle's history of speed and acceleration, which, in turn, enables the prediction of the vehicle's (immediate) future power requirements. That is, the power requirement prediction is achieved by combining a real-time power requirement model and the estimation of the vehicle's acceleration and speed. The proposed approach predicts closer to the actual required power than a widely-used heuristic approach that uses measured power demand, by up to 69.2%.
In global real-time multiprocessor scheduling, a recent analysis technique for Task-level Fixed-Priority (TFP) scheduling has been shown to outperform many of the analyses for Job-level Fixed-Priority (JFP) scheduling on average. Since JFP is a generalization of TFP scheduling, and the TFP analysis technique itself has been adapted from an earlier JFP analysis, this result is counter-intuitive and in our opinion highlights the lack of good JFP scheduling techniques. Towards generalizing the superior TFP analysis to JFP scheduling, we propose the Smallest Pseudo-Deadline First (SPDF) JFP scheduling algorithm. SPDF uses a simple task-level parameter called pseudo-deadline to prioritize jobs, and hence can behave as a TFP or JFP scheduler depending on the values of the pseudodeadlines. This natural transition from TFP to JFP scheduling has enabled us to incorporate the superior TFP analysis technique in an SPDF schedulability test. We also present a pseudo-deadline assignment algorithm for SPDF scheduling that extends the well-known Optimal Priority Assignment (OPA) algorithm for TFP scheduling. We show that our algorithm is optimal for the derived schedulability test, and also present a heuristic to overcome the computational complexity issue of the optimal algorithm. Our simulation results show that the SPDF algorithm with the new analysis significantly outperforms state-of-the-art TFP and JFP analysis.
Interest in real-time multiprocessor scheduling has been rekindled as multi-core chips are increasingly used for embedded real-time systems. While tasks may be preemptive or non-preemptive (due to their transactional operations), deadline guarantees are usually made only for those task sets in each of which all tasks are preemptive or non-preemptive, not a mixture thereof, i.e., all or nothing. In this paper, we develop a schedulability analysis framework that guarantees the timing requirements of a given task set in which a task can be either preemptive or non-preemptive. As an example, we apply this framework to the prioritization policy of EDF (Earliest Deadline First), yielding schedulability tests of mpn-EDF (Mixed Preemptive/Non-preemptive EDF), which is a generalization of both fp-EDF (fully-preemptive EDF) and np-EDF (non-preemptive EDF). In addition to their deadline guarantees for any task set that is composed of a mixture of preemptive and non-preemptive tasks, the tests outperform the existing schedulability tests of np-EDF (a special case of mpn-EDF) by up to 109.1%. Using these tests, we also improve schedulability by disallowing preemptions of some preemptive tasks. For this, we develop an algorithm that optimally disallows preemption of a preemptive task under a certain assumption, and demonstrate via simulation that the algorithm discovers up to 30.9% additional task sets that are schedulable with the proposed scheduling scheme, but not with fp-EDF or np-EDF.
LLF (Least Laxity First) scheduling, which assigns a higher priority to a task with a smaller laxity, has been known as an optimal preemptive scheduling algorithm on a single processor platform. However, little work has been made to illuminate its characteristics upon multiprocessor platforms. In this paper, we identify the dynamics of laxity from the system's viewpoint and translate the dynamics into LLF multiprocessor schedulability analysis. More specifically, we first characterize laxity properties under LLF scheduling, focusing on laxity dynamics associated with a deadline miss. These laxity dynamics describe a lower bound, which leads to the deadline miss, on the number of tasks of certain laxity values at certain time instants. This lower bound is significant because it represents invariants for highly dynamic system parameters (laxity values). Since the laxity of a task is dependent of the amount of interference of higher-priority tasks, we can then derive a set of conditions to check whether a given task system can go into the laxity dynamics towards a deadline miss. This way, to the author's best knowledge, we propose the first LLF multiprocessor schedulability test based on its own laxity properties. We also develop an improved schedulability test that exploits slack values. We mathematically prove that the proposed LLF tests dominate the state-of-the-art EDZL tests. We also present simulation results to evaluate schedulability performance of both the original and improved LLF tests in a quantitative manner.
ISSN: 0922-6443
It is generally challenging to determine end-to-end delays of applications for maximizing the aggregate system utility subject to timing constraints. Many practical approaches suggest the use of intermediate deadline of tasks in order to control and upper-bound their end-to-end delays. This paper proposes a unified framework for different time-sensitive, global optimization problems, and solves them in a distributed manner using Lagrangian duality. The framework uses global viewpoints to assign intermediate deadlines, taking resource contention among tasks into consideration. For soft real-time tasks, the proposed framework effectively addresses the deadline assignment problem while maximizing the aggregate quality of service. For hard real-time tasks, we show that existing heuristic solutions to the deadline assignment problem can be incorporated into the proposed framework, enriching their mathematical interpretation.
ISSN: 0164-1212
It has been widely studied how to schedule real-time tasks on multiprocessor platforms. Several studies have developed optimal scheduling policies for implicit deadline task systems. So far however, studies have failed to develop effective scheduling strategies for more general task systems such as constrained deadline tasks. We argue that a narrow focus on deadline satisfaction (urgency) is the primary reason for this lack of success. In particular, few studies have considered the impact on scheduling of the restriction that a job cannot simultaneously execute on more than one core (parallelism). In this paper we look at one such simple, but effective, characterization of urgency and parallelism ? the zero laxity first policy (ZL policy). We study in detail how beneficial the ZL policy is to schedulability. We then develop an improved schedulability test for any algorithm that employs the ZL policy, and prove that the test dominates previously known tests. Our simulation results show that the improved ZL schedulability test outperforms the existing ones.
ISSN: 0164-1212
It is widely assumed that scheduling real-time tasks becomes more difficult as their deadlines get shorter. With deadlines shorter, however, tasks potentially compete less with each other for processors, and this could produce more contention-free slots at which the number of competing tasks is smaller than or equal to the number of available processors. This paper presents a policy (called CF policy) that utilizes such contention-free slots effectively. This policy can be employed by any work-conserving, preemptive scheduling algorithm, and we show that any algorithm extended with this policy dominates the original algorithm in terms of schedulability. We also present improved schedulability tests for algorithms that employ this policy, based on the observation that interference from tasks is reduced when their executions are postponed to contention-free slots. Finally, using the properties of the CF policy, we derive a counter-intuitive claim that shortening of task deadlines can help improve schedulability of task systems. We present heuristics that effectively reduce task deadlines for better scheduability without performing any exhaustive search.
LLF (Least Laxity First) scheduling, which assigns a higher priority to a task with smaller laxity, has been known as an optimal preemptive scheduling algorithm on a single processor platform. However, its characteristics upon multiprocessor platforms have been little studied until now. Orthogonally, it has remained open how to efficiently schedule general task systems, including constrained deadline task systems, upon multiprocessors. Recent studies have introduced zero laxity (ZL) policy, which assigns a higher priority to a task with zero laxity, as a promising scheduling approach for such systems (e.g., EDZL). Towards understanding the importance of laxity in multiprocessor scheduling, this paper investigates the characteristics of ZL policy and presents the first ZL schedulability test for any work-conserving scheduling algorithm that employs this policy. It then investigates the characteristics of LLF scheduling, which also employs the ZL policy, and derives the first LLF-specific schedulability test on multiprocessors. It is shown that the proposed LLF test dominates the ZL test as well as the state-of-art EDZL test.
In distributed soft real-time systems, maximizing the aggregate quality-of-service (QoS) is a typical system-wide goal, and addressing the problem through distributed optimization is challenging. Subtasks are subject to unpredictable failures in many practical environments, and this makes the problem much harder. In this paper, we present a robust optimization framework for maximizing the aggregate QoS in the presence of random failures. We introduce the notion of K-failure to bound the effect of random failures on schedulability. Using this notion we define the concept of K-robustness that quantifies the degree of robustness on QoS guarantee in a probabilistic sense. The parameter K helps to tradeoff achievable QoS versus robustness. The proposed robust framework produces optimal solutions through distributed computations on the basis of Lagrangian duality, and we present some implementation techniques. Our simulation results show that the proposed framework can probabilistically guarantee sub-optimal QoS which remains feasible even in the presence of random failures.
Address allocation is an essential part in maintaining a mobile ad hoc network (MANET) effectively, and several address allocation schemes have been proposed. In this paper, we present a set of analytical models to evaluate the efficiency of address allocation schemes. The derived models quantitatively characterize the efficiency of four popular address allocation schemes in terms of latency and communication overhead. Through the analysis, we achieve numerical results that show the impact of network parameters on the efficiency of these schemes. We also conduct simulations and compare with analytical results to validate our models. The analytical model developed in this paper is able to more accurately predict the performance of address allocation schemes over a various range of loss rates and would be useful in providing more insights for the study of an efficient address allocation scheme in MANETs. To our understanding, this is the first attempt in mathematically investigating the performance of addressing schemes in ad hoc networks.
ISSN: 0018-9545
Machine learning methods have been used to solve real-time scheduling problems but none has yet made an architecture that utilizes influences between real-time tasks as input features. This paper proposes a novel approach to partitioned scheduling in real-time systems using graph machine learning. We present a graph representation of real-time task sets that enable graph machine-learning schemes to capture the influence between real-time tasks. By using a graph attention network (GAT) with this method, our model successfully partitioned-schedule task sets that were previously deemed unschedulable by state-of-the-art partitioned scheduling algorithms. The GAT is used to establish relationships between nodes in the graph, which represent real-time tasks, and to learn how these relationships affect the schedulability of the system. tests.
ISSN: 1943-0663
As many safety- or mission-critical electric systems become equipped with batteries, we need to achieve seemingly conflicting goals: G1) timely execution of power-consuming tasks (for safety or mission) while G2) minimizing battery aging (for system sustainability). To this end, this paper proposes a novel scheduling framework for a set of power-consuming real-time tasks, which efficiently utilizes run-time slack (i.e., system idle time identified at run-time) to find the tasks' schedule that achieves both G1 and G2. The proposed framework is not only applicable to any existing prioritization policy (e.g., EDF and FP) but has also been proven to reduce battery aging by up to 32.6% without compromising G1.
ISSN: 1383-7621
One of the important design issues for time-critical embedded systems is to derive necessary conditions that meet all job deadlines invoked by a set of recurring real-time tasks under a computing resource (called feasibility). To this end, existing studies focused on how to derive a tight lower-bound of execution requirement (i.e., demand) of a target set of real-time tasks. In this paper, we address the following question regarding the supply provided by a multiprocessor resource: is it possible for a real-time task set to always utilize all the provided supply? We develop a systematic approach that i) calculates the amount of supply proven unusable, ii) finds a partial schedule that yields a necessary condition to minimize the amount of unusable supply, and iii) uses the partial schedule to further reclaim unusable supply. While the systematic approach can be applied to most (if not all) recurring real-time task models, we show two examples how the approach can yield tight necessary feasibility conditions for the sequential task model and the gang scheduling model. We demonstrate the proposed approach finds a number of additional infeasible task sets which have not been proven infeasible by any existing studies for the task models.
ISSN: 1383-7621
In this paper, we focus on splitting a task into multiple sub-tasks to improve schedulability performance for a set of tasks subject to timing constraints. Targeting FPS (Fixed Priority Scheduling) as a scheduling algorithm and RTA (Response Time Analysis) as a schedulability analysis on a multiprocessor platform, we develop a systematic method to utilize the task split, which addresses (i) how to apply the task split without violating the timing requirement of the original tasks and (ii) how to apply each task so as to make an unschedulable task set to be schedulable. Our simulation results demonstrate that RTA with the method finds 16.1%?19.4% (on average) additional task sets that were not proven schedulable by the vanilla RTA. tests.
ISSN: 1383-7621
The spread of COVID-19 issues high demand on measuring body temperature, which necessitates thermometers. To alleviate a burden to equip/carry thermometers, this paper develops a framework “TherMobile” that measures body temperature using a commercial-off-the-shelf smartphone that most people carry everywhere. Considering that most (if not all) smartphones have a temperature sensor on its battery, we utilize heat transfer from a body part that makes contact with the smartphone, to the smartphone battery. To this end, we collect a time series of the smartphone battery temperature for different pairs of the initial temperature of the smartphone battery and the temperature of a body part, and then classify them. To enable the data collection and classification to infer the temperature of the body part, we address important practical issues, including how to gather data for different target temperatures of a body part (although human body temperature is not controllable), and how to minimize a burden for individual users to gather all necessary data. Our experiments demonstrate that “TherMobile” achieves 90.0% accuracy of measuring body temperature with 1.0°C granularity, enabling a commercial-off-the-shelf smartphone to substitute for a thermometer without any additional hardware. tests.
ISSN: 1558-1748
In real-time embedded systems, NP-EDF (Non-Preemptive Earliest Deadline First) is one of the most popular scheduling algorithms to offer timing guarantees of a set of nonpreemptive real-time jobs (tasks). While most existing schedulability tests for NP-EDF on a multiprocessor platform have paid attention to improving schedulability performance at the expense of increasing time-complexity, only a few studies can be used for the situation where low time-complexity is critical. In this paper, based on an existing low time-complexity schedulability test for NP-EDF, we develop schedulability tests that improve schedulability performance but maintain low time-complexity. Our experiments show that the proposed schedulability tests improve schedulability performance up to 592.4%, compared to the existing low time-complexity one, and they can find some additional task sets schedulable by NP-EDF, which cannot be covered by existing high time-complexity NP-EDF schedulability tests.
ISSN: 1943-0663
Fixed-Priority Scheduling (FPS), due to its simplicity to implement, has been one of the most popular scheduling algorithms for real-time embedded systems equipped with multiprocessor platforms. While there have been many studies that find sufficient conditions for a given task set to be feasible (schedulable) by FPS with a proper priority assignment, the other direction (i.e., finding infeasible task sets) has not been studied. In this letter, we address a necessary feasibility condition that judges a given task set to be infeasible under FPS with every priority assignment on multiprocessor platforms. To this end, we derive useful properties for the condition, and develop the first infeasibility test for FPS on multiprocessor platforms. Via simulations, we show that the proposed infeasibility test discovers a number of FPS-infeasible task sets which are not proven FPS-infeasible by any existing studies.
ISSN: 1943-0663
Parallelization of tasks and efficient utilization of processors are considered important and challenging in operating large-scale real-time systems. Recently, deep reinforcement learning (DRL) was found to provide effective solutions to various combinatorial optimization problems. In this paper, inspired by recent achievements in DRL, we employ DRL techniques for scheduling a directed acyclic graph (DAG) task in which a set of non-preemptive subtasks are specified by precedence conditions among them. We propose a DRL-based priority assignment model for scheduling a DAG task on a multiprocessor system, named GoSu , which adapts a graph convolution network (GCN) to process a complex interdependent task structure and minimize the makespan of a DAG task. Our proposed model makes use of both temporal and structural features in a DAG to effectively learn a priority-based scheduling policy via GCN and policy gradient methods. With comprehensive evaluations, we verify that our model shows comparable performance to several state-of-the-art DAG task scheduling algorithms, and outperforms them by 2~3% in the slowdown of achieved makespans particularly in nontrivial system configurations where workloads are neither too small nor heavy compared to the given number of processors. We also analyze the priority assignment behaviors of our model by leveraging a regression method that imitates the learned policy of the model.
ISSN: 2169-3536
In a traditional real-time system, the major requirement is to finish every real-time task within its predefined time. However, as a modern real-time system is connected to external networks and its subsystems are developed by different vendors, it becomes an important requirement to address the problem of information leakage on resources shared by different real-time tasks. This triggers studies that incorporate security mechanisms into the real-time scheduling. While it is one of the most well-known approaches to add the flush task (FT) mechanism and analyze its effect on timing guarantees, the existing FT approaches have been limited to a real-time system that employs at most one resource shared by real-time tasks executed on a uniprocessor platform. In this paper, we propose a flush task incorporated priority-inheritance protocol (FT-PIP) for global fixed-priority multiprocessor scheduling and develop its schedulability analysis, which is the first attempt that not only (a) supports timing guarantees but also (b) satisfies security constraints for (c) a multiprocessor platform with multiple shared resources. Via simulations, we demonstrate that FT-PIP and its schedulability analysis are effective in achieving both (a) and (b) for (c).
ISSN: 2169-3536
While an SSD (Solid State Drive) has been widely used for storage in many computing systems due to its small average latency, how to provide timing guarantees of a delay-sensitive (real-time) task on a real-time system equipped with an SSD has not been fully explored. A recent study has proposed a work-constraining I/O scheduler, called K2, which has succeeded in reducing the tail latency of a real-time task at the expense of compromising the total bandwidth for real-time and non-real-time tasks. Although the queue length bound parameter of the K2 scheduler is a key to regulate the tradeoff between a decrease in the tail latency of a real-time task and an increase in penalty of the total bandwidth, the parameter's impact on the tradeoff has not been thoroughly investigated. In particular, no studies have addressed how the case of a fully occupied SSD that incurs garbage collection changes the performance of the K2 scheduler in terms of the tail latency of the real-time task and the total bandwidth. In this paper, we systematically analyze the performance of the K2 scheduler for different I/O operation types, based on experiments on Linux. We investigate how the performance is changed on a fully occupied SSD due to garbage collection. Utilizing the investigation, we draw general guidelines on how to select a proper setting of the queue length bound for better performance. Finally, we propose how to apply the guidelines to achieve target objectives that optimize the tail latency of the real-time task and the total bandwidth at the same time, which has not been achieved by previous studies.
ISSN: 2079-9292
Non-preemptive scheduling is essential to tasks that inherently disallow any preemption and useful for tasks that exhibit extremely large preemption/migration overhead; however, studies of non-preemptive scheduling have not matured for real-time tasks subject to timing constraints. In this paper, we propose an improved schedulability test for non-preemptive fixed-priority scheduling (NP-FP), which offers timing guarantees for a set of real-time tasks executed on a multiprocessor platform. To this end, we first carefully investigate the NP-FP properties, and present an observation why the existing technique is pessimistic in calculating interference from higher-priority tasks. We then develop a new technique that tightly upper-bounds the amount of the interference, and show how to incorporate the technique into the existing schedulability test. Via simulations, we demonstrate that our proposed test improves schedulability performance of NP-FP up to 18%, compared to the state-of-the-art existing tests.
ISSN: 1943-0663
Although many studies have attempted to detect the hand postures of a mobile device to utilize these postures as a user interface, they either require additional hardware or can differentiate a limited number of grips only if there is a touch event on the mobile device's screen. In this paper, we propose a novel grip sensing system, called SmartGrip, which allows a mobile device to detect different hand postures without any additional hardware and a screen touch event. SmartGrip emits carefully designed sound signals and differentiates the propagated signals distorted by different user grips. To achieve this, we analyze how a sound signal propagates from the speaker to the microphone of a mobile device and then address three key challenges: sound structure design, volume control, and feature extraction and classification. We implement and evaluate SmartGrip on three Android mobile devices. With six representative grips, SmartGrip exhibits 93.1% average accuracy for ten users in an office environment. We also demonstrate that SmartGrip operates with 83.5 to 98.3% accuracy in six different (noisy) locations. Further demonstrating the feasibility of SmartGrip as a user interface, we develop an Android application that exploits SmartGrip, validating its practical usage.
ISSN: 1617-4909
Recently, deep reinforcement learning (RL) technologies have been considered as a feasible solution for tackling combinatorial problems in various research and engineering areas. Motivated by this recent success of RL-based approaches, in this paper, we focus on how to utilize RL technologies in the context of real-time system research. Specifically, we first formulate the problem of fixed-priority assignments for multi-processor real-time scheduling, which has long been considered challenging in the real-time system community, as a combinatorial problem. We then propose the RL-based priority assignment model Panda that employs (i) a taskset embedding mechanism driven by attention-based encoder-decoder deep neural networks, hence enabling to efficiently extract useful features from the dynamic relation of periodic tasks. We also present two optimization schemes tailored to adopt RL for real-time task scheduling problems: (ii) the response time analysis (RTA)-based policy gradient RL and guided learning schemes, which facilitate the training processes of the Panda model. To the best of our knowledge, our approach is the first to employ RL for real-time task scheduling. Through various experiments, we show that Panda is competitive with well-known heuristic algorithms for real-time task scheduling upon a multi-processor platform, and it often outperforms them in large-scale non-trivial settings, e.g., achieving an average 7.7% enhancement in schedulability ratio for a testing system configuration of 64-sized tasksets and an 8-processor platform.
ISSN: 2169-3536
In real-time distributed systems, it is important to provide offline guarantee for an upper-bound of each real-time task's end-to-end delay, which has been achieved by assigning proper intermediate deadlines of individual real-time tasks at each node. Although existing studies have succeeded to utilize mathematical theories of distributed computation/control for intermediate deadline assignment, they have assumed that every task operates in a cooperative manner, which does not always hold for real-worlds. In addition, existing studies have not addressed how to exploit a trade-off between end-to-end delay fairness among real-time tasks and performance for minimizing aggregate end-to-end delays. In this paper, we recapitulate an existing cooperative distributed framework, and propose a non-cooperate distributed framework that can operate even with selfish tasks, each of which is only interested in minimizing its own end-to-end delay regardless of achieving the system goal. We then propose how to design utility functions that allow the real-time distributed system to exploit the trade-off. Finally, we demonstrate the validity of the cooperative and non-cooperative frameworks along with the designed utility functions, via simulations.
ISSN: 2227-7390
Recently, traditional real-time systems that played a dedicated role in a limited environment have been evolving to interact with dynamically varying environments. In modern real-time systems, characteristics of real-time tasks such as computational demand and resource allocation can vary over time according to different circumstances, which is referred to as mode transition. In this paper, we focus on the problem of timing guarantees of a set of multi-mode tasks associated with mode transitions and develop an offline schedulability analysis, which does not require any online information; this is an important problem in the real-time systems area. The proposed schedulability analysis not only generalizes an existing framework designed for single-mode tasks, but also significantly improves the state-of-the-art framework designed for multi-mode tasks. Building on the proposed analysis, we also address the problem of enforcing the order of tasks within each mode transition and propose a task-level transition order assignment algorithm, yielding further improvement in schedulability performance. Through simulations, our proposed framework is shown to improve schedulability up to 777.3% over an existing schedulability analysis for multi-mode tasks, depending on the experiment setting under our evaluation environment.
ISSN: 2169-3536
As different batteries exhibit their own advantages and disadvantages, a single-type battery system for a mobile embedded system (such as smartphones and tablet PC) cannot overcome the inherent limitations of its target battery. For example, although the lithium cobalt oxide (LCO) battery is the most popular battery for mobile embedded systems due to its high energy density, mobile embedded systems equipped with LCO batteries suffer from rapid capacity degradation. In this letter, we target heterogeneous batteries in a mobile embedded system and propose a method for minimizing the capacity degradation of the batteries. To this end, we first analyze factors that affect capacity degradation, and choose dominant factors that are controllable in a mobile embedded system. We then build a battery degradation model considering the factors, and finally develop a battery scheduling algorithm that minimizes capacity degradation using the degradation model. Our evaluation with real experiments and simulations demonstrates the effectiveness of the proposed algorithm in minimizing capacity degradation.
ISSN: 1943-0663
In a real-time system, a series of jobs invoked by each task should finish its execution before its deadline, and EDF (Earliest Deadline First) is one of the most popular scheduling algorithms to meet such timing constraints of a set of given tasks. However, EDF is known to be ineffective in meeting timing constraints for non-preemptive tasks (which disallow any preemption) when the system does not know the future job release patterns of the tasks. In this paper, we develop a scheduling algorithm for a real-time system with a symmetry multiprocessor platform, which requires only limited information about the future job release patterns of a set of non-preemptive tasks, called LCEDF. We then derive its schedulability analysis that provides timing guarantees of the non-preemptive task set on a symmetry multiprocessor platform. Via simulations, we demonstrate the proposed schedulability analysis for LCEDF significantly improves the schedulability performance in meeting timing constraints of a set of non-preemptive tasks up to 20.16%, compared to vanilla non-preemptive EDF.
ISSN: 2073-8994
Smartphone users often want to customize the positions and functions of physical buttons to accommodate their own usage patterns; however, this is unfeasible for electronic mobile devices based on COTS (Commercial Off-The-Shelf) due to high production costs and hardware design constraints. In this letter, we present the design and implementation of customized virtual buttons that are localized using only common built-in sensors of electronic mobile devices. We develop sophisticated strategies firstly to detect when a user taps one of the virtual buttons, and secondly to locate the position of the tapped virtual button. The virtual-button scheme is implemented and demonstrated in a COTS-based smartphone. The feasibility study shows that, with up to nine virtual buttons on five different sides of the smartphone, the proposed virtual buttons can operate with greater than 90% accuracy.
ISSN: 0916-8508
While recent studies addressed security attacks in real-time embedded systems, most of them assumed prior knowledge of parameters of periodic tasks, which is not realistic under many environments. In this paper, we address how to infer task parameters, from restricted information obtained by simple system monitoring. To this end, we first develop static properties that are independent of inference results and therefore applied only once in the beginning. We further develop dynamic properties each of which can tighten inference results by feeding an update of the inference results obtained by other properties. Our simulation results demonstrate that the proposed inference framework infers task parameters for RM (Rate Monotonic) with reasonable tightness; the ratio of exactly inferred task periods is 95.3% and 65.6%, respectively with low and high task set use. The results also discover that the inference performance varies with the monitoring interval length and the task set use.
ISSN: 2079-9292
While conventional studies on real-time systems have mostly considered the real-time constraint of real-time systems only, recent research initiatives are trying to incorporate a security constraint into real-time scheduling due to the recognition that the violation of either of two constrains can cause catastrophic losses for humans, the system, and even environment. The focus of most studies, however, is the single-criticality systems, while the security of mixed-criticality systems has received scant attention, even though security is also a critical issue for the design of mixed-criticality systems. In this paper, we address the problem of the information leakage that arises from the shared resources that are used by tasks with different security-levels of mixed-criticality systems. We define a new concept of the security constraint employing a pre-flushing mechanism to cleanse the state of shared resources whenever there is a possibility of the information leakage regarding it. Then, we propose a new non-preemptive real-time scheduling algorithm and a schedulability analysis, which incorporate the security constraint for mixed-criticality systems. Our evaluation demonstrated that a large number of real-time tasks can be scheduled without a significant performance loss under a new security constraint.
ISSN: 0916-8532
RTA (Response time analysis) is a popular technique to guarantee timing requirements for a real-time system, and therefore the RTA framework has been widely studied for popular scheduling algorithms such as EDF (Earliest Deadline First) and FP (Fixed Priority). While a number of extended techniques of RTA have been introduced, some of them cannot be used since they have not been proved and evaluated in terms of their correctness and empirical performance. In this letter, we address the state of the art technique of slack reclamation of the existing generic RTA framework for multiprocessors. We present its mathematical proof of correctness and empirical performance evaluation, which have not been revealed to this day.
ISSN: 0916-8532
In this paper, we propose a simple, but effective scheduling framework for EDF and RM, which reduces the number of preemptions by simply introducing a dummy task. We first observe useful preemption behavior under EDF and RM, leading to an interesting finding: an effective way to reduce the number of preemptions is to prevent jobs of a task with the smallest task period from preempting other jobs upon their release. To achieve this, we add a dummy task that invokes its job only when a newly-released job of the task with the smallest task period has a higher priority than the currently-executing job. Then, the currently-executing job can continue its execution without getting preempted by inheriting the priority of the dummy job. Since adding the dummy task can make a schedulable task set unschedulable, we propose how to set the dummy task's parameters without compromising schedulability. In addition to the negligible overhead of this framework due to its simplicity, it holds an important property that does not increase the number of preemptions of any task set, compared to the original scheduling algorithm, which has not been achieved by existing studies. We also demonstrate via simulation that the proposed framework effectively reduces the number of preemptions under EDF and RM.
ISSN: 0010-485X
While conventional studies on real-time systems have mostly considered the real-time constraint of real-time systems only, recent research initiatives are trying to incorporate a security constraint into real-time scheduling due to the recognition that the violation of either of two constrains can cause catastrophic losses for humans, the system, and even environment. The focus of most studies, however, is the single-criticality systems, while the security of mixed-criticality systems has received scant attention, even though security is also a critical issue for the design of mixed-criticality systems. In this paper, we address the problem of the information leakage that arises from the shared resources that are used by tasks with different security-levels of mixed-criticality systems. We define a new concept of the security constraint employing a pre-flushing mechanism to cleanse the state of shared resources whenever there is a possibility of the information leakage regarding it. Then, we propose a new non-preemptive real-time scheduling algorithm and a schedulability analysis, which incorporate the security constraint for mixed-criticality systems. Our evaluation demonstrated that a large number of real-time tasks can be scheduled without a significant performance loss under a new security constraint.
ISSN: 0916-8532
Real-time systems (RTS) are characterized by tasks executing in a timely manner to meet its deadlines as a real-time constraint. Most studies of RTS have focused on these criteria as primary design points. However, recent increases in security threats to various real-time systems have shown that enhanced security support must be included as an important design point, retro-fitting such support to existing systems as necessary. In this paper, we propose a new pre-flush technique referred to as flush task reservation for FP scheduling (FTRFP) to conditionally sanitize the state of resources shared by real-time tasks by invoking a flush task (FT) in order to mitigate information leakage/corruption of real-time systems. FTR-FP extends existing works exploiting FTs to be applicable more general scheduling algorithms and security model. We also propose modifications to existing real-time scheduling algorithms to implement a pre-flush technique as a security constraint, and analysis technique to verify schedulability of the real-time scheduling. For better analytic capability, our analysis technique provides a count of the precise number of preemptions that a task experiences offline. Our evaluation results demonstrate that our proposed schedulability analysis improves the performance of existing scheduling algorithms in terms of schedulability and preemption cost.
ISSN: 1582-7445
Collision-free transmission and efficient data transfer between nodes can be achieved through a set of channels in multichannel wireless sensor networks (MWSNs). While using multiple channels, we have to carefully consider channel interference, channel and time slot (resources) optimization, channel switching delay, and energy consumption. Since sensor nodes operate on low battery power, the energy consumed in channel switching becomes an important challenge. In this paper, we propose channel and time slot scheduling for minimal channel switching in MWSNs, while achieving efficient and collision-free transmission between nodes. The proposed scheme constructs a duty-cycled tree while reducing the amount of channel switching. As a next step, collision-free time slots are assigned to every node based on the minimal data collection delay. The experimental results demonstrate that the validity of our scheme reduces the amount of channel switching by 17.5%, reduces energy consumption for channel switching by 28%, and reduces the schedule length by 46%, as compared to the existing schemes.
ISSN: 1424-8220
Collision-free transmission and efficient data transfer between nodes can be achieved through a set of channels in multichannel wireless sensor networks (MWSNs). While using multiple channels, we have to carefully consider channel interference, channel and time slot (resources) optimization, channel switching delay, and energy consumption. Since sensor nodes operate on low battery power, the energy consumed in channel switching becomes an important challenge. In this paper, we propose channel and time slot scheduling for minimal channel switching in MWSNs, while achieving efficient and collision-free transmission between nodes. The proposed scheme constructs a duty-cycled tree while reducing the amount of channel switching. As a next step, collision-free time slots are assigned to every node based on the minimal data collection delay. The experimental results demonstrate that the validity of our scheme reduces the amount of channel switching by 17.5%, reduces energy consumption for channel switching by 28%, and reduces the schedule length by 46%, as compared to the existing schemes.
ISSN: 0013-5194
Time-predictability is the most important requirement for a real-time system, and researchers have therefore paid attention to the duration between the arrival and completion of a real-time task, called response time. RTA (Response Time Analysis) studies, however, rely on the same technique, yielding room for further improvement, especially regarding multiprocessor platforms. For this paper, we investigated the properties of an existing utilization-based schedulability analysis for global EDF (Earliest Deadline First) on a multiprocessor platform, and developed a new RTA technique based on the corresponding properties, which calculates the response times of tasks in task sets deemed schedulable by the existing analysis. We demonstrated through simulations that our proposed RTA technique not only calculates response times that are less pessimistic than those of the existing approach, but also successfully derives response times that cannot be obtained by the existing approach.
ISSN: 1383-7621
Schedulability analysis has been widely studied to provide offline timing guarantees for a set of real-time tasks. The so-called limited carry-in technique, which can be orthogonally incorporated into many different multi-core schedulability analysis methods, was originally introduced for Earliest Deadline First (EDF) scheduling to derive a tighter bound on the amount of interference of carry-in jobs at the expense of investigating a pseudo-polynomial number of intervals. This technique has been later adapted for Fixed-Priority (FP) scheduling to obtain the carry-in bound efficiently by examining only one interval, leading to a significant improvement in multi-core schedulability analysis. However, such a successful result has not yet been transferred to any other non-FP scheduling algorithms. Motivated by this, this paper presents a generic limited carry-in technique that is applicable to any work-conserving algorithms. Specifically, this paper derives a carry-in bound in an algorithm-independent manner and demonstrates how to apply the bound to existing non-FP schedulability analysis methods for better schedulability.
ISSN: 1383-7621
In wireless networks, collision is a major factor of performance degradation. In this letter, we propose a scheme for reducing collision in IEEE 802.11 networks. Each node can avoid collision by maintaining a disjoint set of time slots for transmission. Through simulation, we show that the proposed scheme is effective to reduce collision even in the presence of hidden nodes.
ISSN: 1089-7798
Amongst densely populated areas such as New York, Hong Kong, Seoul and etc., apartments and condominiums grew to be the most commonly used type of housing units. Throughout the years we have witnessed many issues within the residents living in such units suffer from apartment noise. Lousy neighbors are not the only cause of such problems but the way houses were built, more specifically type and thickness of the flooring materials, determines the amount of noise transfer from one household to the other. In this paper, we show feasibility of classifying flooring materials using COTS (Commercial Off-The-Shelf) mobile devices. We exert each flooring materials propagating synthetic chirps in various ways. Our approach makes it possible to distinguish three types of target flooring materials (wood, polystyrene and concrete) and can also give similar results when the target materials are piled on top of one another. To this end we i) design an acoustic signal which effectively differentiates each target flooring materials, ii) gather sound samples propagated from the target materials, and iii) provide a classification methodology using methods such as SVM and KNN. Finally, we discuss the necessities to achieve the final goal of identifying flooring materials and their thickness.
Body temperature measurement is one of the simple, yet effective ways to diagnose a variety of diseases, and its importance is growing in the era of COVID-19. In this paper, we show feasibility of measuring body temperature using a COTS (Commercial Off-The-Shelf) mobile device. To this end, we utilize the battery temperature sensor embedded in most COTS mobile devices, and analyze how the battery temperature varies according to different temperatures of an external object that makes contact with the target mobile device (e.g., a hand holding the device). Our approach makes it possible to estimate the external object’s temperature within 0.5 C of accuracy, by (i) data collection of the battery temperature variation for some pairs of the initial battery temperature and the target object temperature, (ii) virtual data generation for the other pairs, and (iii) temperature estimation via classification of the collected data and generated ones.
Different from a general-purpose system, a real-time system requires stringent timing guarantees. While existing offline analysis techniques can provide timing guarantees using the worst-case execution time (WCET) of individual tasks, a variation of actual execution time makes it difficult to build covert timing channel. In this paper, we first present a novel covert timing channel, which considers actual execution time distribution of tasks and controls execution time to leak data between conspirators; we demonstrate that it is possible to leak data in real-time systems. Second, we suggest two enhancing techniques called S-R LCM (sender-receiver least common multiple) and noise area to reduce noise in communication. Through simulations, we demonstrate that our covert timing channel can serve trade-off between transmission speed and accuracy; that is, it shows average 50.2%, 54.6% and 51.3% accuracy for 100 test cases with thresholds 0, 1.4 and 2.8. Average 58.4% accuracy is accomplished with best threshold values for 100 test cases, and the maximum accuracy for a single test case is recorded 100.0%.
In real-time systems, timing guarantees on real-time tasks are the most important requirement. The requirement, however, causes inefficient resource usage due to over-estimated (but safe) task execution time, which brings the advent of the concept of Mixed-Criticality (MC). In this paper, we improve an existing schedulability analysis of Fixed-Priority (FP) scheduling on MC systems. Our evaluation results demonstrate the effectiveness of our analysis in terms of schedulability.
While hand grips are important to understand the intent of smartphone users, existing studies on hand grip detection either require additional hardware or exhibit limitations on the type/number of grips. In this paper, we propose a novel grip sensing system that enables a smartphone to detect various user-defined hand grips without any additional hardware. Our system emits a carefully-designed (inaudible) sound signal, and records the sound signal modified by an individual grip. The recorded sound signal is transformed into a unique sound signature through feature extraction process, and then SVM (Support Vector Machine) classifies the sound signature so as to identify the signature as one of pre-defined grips. With six representative grips, we demonstrate that our system exhibits 93.0% average accuracy for ten different users. Beyond this feasibility demonstration, our ongoing work is not only to improve the accuracy, but also to adapt our system to various real environments.
A large-scale Battery Management System (BMS) used in Electric Vehicles (EVs) and energy storage systems is a typical Cyber-Physical System (CPS) application in that scheduling of battery charge, discharge, and rest (i.e., cyber part) can significantly improve BMS performance under understanding and controlling battery characteristics (i.e., physical part). Therefore, the CPS community has paid attention to BMSes, e.g., ICCPS [4, 5, 3] and the CPS track in RTSS [7, 2, 6, 1].
The current industry trend is towards using Commercially available Off-The-Shelf (COTS) based multicores for developing real-time embedded systems, as opposed to the usage of custom-made hardware. In typical implementation of such COTS-based multicores, multiple cores access the main memory via a shared bus. This often leads to contention on this shared channel, which results in an increase of the response time of the tasks. Analyzing this increased response time, considering the contention on the shared bus, is challenging on COTS-based systems mainly because bus arbitration protocols are often undocumented and the exact instants at which the shared bus is accessed by tasks are not explicitly controlled by the operating system scheduler; they are instead a result of cache misses. This paper makes three contributions towards analyzing tasks scheduled on COTS-based multicores. Firstly, we describe a method to model the memory access patterns of a task. Secondly, we apply this model to analyze the worstcase response time for a set of tasks. Although the required parameters to obtain the request profile can be obtained by static analysis, we provide an alternative method to experimentally obtain them by using performance monitoring counters (PMCs). We also compare our work against an existing approach and show that our approach outperforms it by providing tighter upper-bound on the number of bus requests generated by a task.
Contention on the memory bus in COTS based multi-core systems is becoming a major determining factor of the execution time of a task. Analyzing this extra execution time is non-trivial because (i) bus arbitration protocols in such systems are often undocumented and (ii) the times when the memory bus is requested to be used are not explicitly controlled by the operating system scheduler; they are instead a result of cache misses. We present a method for finding an upper bound on the extra execution time of a task due to contention on the memory bus in COTS based multicore systems. This method makes no assumptions on the bus arbitration protocol (other than assuming that it is work-conserving).
It has been widely studied how to schedule real-time tasks on multiprocessor platforms. Several studies find optimal scheduling policies for implicit deadline task systems, but it is hard to understand how each policy utilizes the two important aspects of scheduling real-time tasks on multiprocessors: inter-job concurrency and job urgency. In this paper, we introduce a new scheduling policy that considers these two properties. We prove that the policy is optimal for the special case when the execution time of all tasks are equally one and deadlines are implicit, and observe that the policy is a new concept in that it is not an instance of Pfair or ERfair. It remains open to find a scheduliability condition for general task systems under our scheduling policy.
Regarding to address allocation, a mobile ad hoc network (MANET) may suffer from the lack of address and network partition/merging due to mobility of nodes. In this paper, we propose a new address allocation protocol for dealing with those problems. The proposed protocol is developed based on disjoint address set distribution with binary splitting for scalability, and provides special treatments for resolving the lack of addresses. We also present an effective technique for handling network partition and merging. Through simulation, we show that the proposed protocol is effective to allocate addresses in a MANET with reasonable latency and communication overhead.