Performance maximization of energy-variable self-powered (m, k)-firm real-time systems

Shirazi, Mahmoud; Kargahi, Mehdi; Thiele, Lothar

doi:10.1007/s11241-020-09344-1

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Another series of fault tolerance techniques rely on the (m, k) models, which is originally developed for guaranteeing limited deadline misses for firmed real-time systems, or socalled weakly-hard real-time systems [4], where a task has to meet at least m deadlines, or can miss at most m deadlines, in any of k consecutive jobs 2 . Although the original work [4] applied n m to describe the any n of m, most of the following works utilize the (m, k) to describe the weakly-hard constraints [11], [18], [39], [43]. Afterwards, such (m, k) models are commonly applied for fault tolerance related domain as well to describe the robustness constraints of (control) systems.…”

Section: Related Workmentioning

confidence: 99%

Average Task Execution Time Minimization under (m, k) Soft Error Constraint

Shi

Ueter

Chen

et al. 2023

2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS)

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Safety-critical systems are often subjected to transient faults. Since these transient faults may lead to soft errors that cause catastrophic consequences, error-handling must be addressed by design. Full-protection against faults is too costly in terms of resource usage. A common approach to relax the resource demands and limit the impact of errors is to consider (m, k)-constraints, which requires that at least m jobs out of any k consecutive jobs are error-free. To assure (m, k)-compliance, static patterns are widely used to select the job execution modes, i.e., either in an error-free mode at the cost of increased worst-case execution time or in an error-prone mode with the advantage of less execution time. Although static patterns have been shown to be effective in energy-aware designs, resource over-provision is inevitable due to the relatively low rate of error probability. In this work, we propose two dynamic (and adaptive) approaches that allow the scheduler to opportunistically select execution modes based on the error-history of the past jobs and the actual error probability. We firstly propose a Markov chain based solution if the error-probability is known and static and secondly a reinforcement learning-based approach that can handle unknown error probabilities. Experimental evaluations show that our approaches outperform the state-of-the-art in most of the evaluated cases in terms of average utilization for each task and the overall utilization for multitask systems.1 2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS)

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Section: Related Workmentioning

confidence: 99%

Average Task Execution Time Minimization under (m, k) Soft Error Constraint

Shi

Ueter

Chen

et al. 2023

2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS)

View full text Add to dashboard Cite

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“…Very recently, some works concentrate on semi-HRT applications that accept missed deadlines because of energy starvation. In [52], the incoming source energy is assumed to be predicted with accuracy. The objective of the best effort scheduler is to maximize the performance.…”

Section: Related Workmentioning

confidence: 99%

Earliest Deadline First Scheduling for Real-Time Computing in Sustainable Sensors

Chetto

Osta

2023

Sustainability

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Energy harvesting is a green technology that authorizes small electronic devices to be supplied for perpetual operation. It enables wireless sensors to be integrated in applications that previously were not feasible with conventional battery-powered designs. Intermittent computing and scheduling are the two central aspects of designing a Real-Time Energy Harvesting (RTEH) sensor, generally used to monitor a mission critical process. Traditional scheduling algorithms fail to timely execute the hard deadline tasks because they accommodate no fluctuations in power supply and therefore no intermittent computing. A suitable energy-harvesting-aware scheduling algorithm has been proposed so as to achieve a higher schedulability rate. Unlike the classical EDF (Earliest Deadline First) scheduler, the ED-H algorithm is idling and clairvoyant, with an improved performance in terms of the deadline missing ratio. This paper reviews the main advances in dynamic priority scheduling based on EDF for energy-neutral systems.

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“…The concept of computational awareness is popular in the real-time community [23][24][25][26]. The problem of dynamic scheduling in heterogeneous multiprocessor hardware can be addressed from many perspectives as can be seen in these literature.…”

Section: Related Workmentioning

confidence: 99%

Towards computational awareness in autonomous robots: an empirical study of computational kernels

Sifat

Bharmal

Zeng

et al. 2023

Complex Intell. Syst.

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The potential impact of autonomous robots on everyday life is evident in emerging applications such as precision agriculture, search and rescue, and infrastructure inspection. However, such applications necessitate operation in unknown and unstructured environments with a broad and sophisticated set of objectives, all under strict computation and power limitations. We therefore argue that the computational kernels enabling robotic autonomy must be scheduled and optimized to guarantee timely and correct behavior, while allowing for reconfiguration of scheduling parameters at runtime. In this paper, we consider a necessary first step towards this goal of computational awareness in autonomous robots: an empirical study of a base set of computational kernels from the resource management perspective. Specifically, we conduct a data-driven study of the timing, power, and memory performance of kernels for localization and mapping, path planning, task allocation, depth estimation, and optical flow, across three embedded computing platforms. We profile and analyze these kernels to provide insight into scheduling and dynamic resource management for computation-aware autonomous robots. Notably, our results show that there is a correlation of kernel performance with a robot’s operational environment, justifying the notion of computation-aware robots and why our work is a crucial step towards this goal.

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Performance maximization of energy-variable self-powered (m, k)-firm real-time systems

Cited by 5 publications

References 32 publications

Average Task Execution Time Minimization under (m, k) Soft Error Constraint

Average Task Execution Time Minimization under (m, k) Soft Error Constraint

Earliest Deadline First Scheduling for Real-Time Computing in Sustainable Sensors

Towards computational awareness in autonomous robots: an empirical study of computational kernels

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