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Energy-Aware Partitioned Scheduling of Imprecise Mixed-Criticality Systems
College of Computer Science and Technology, Huaqiao University, China.
College of Computer Science and Technology, Huaqiao University, China.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.ORCID iD: 0000-0003-4228-2774
2023 (English)In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ISSN 0278-0070, E-ISSN 1937-4151, p. 1-1Article in journal (Refereed) Published
Abstract [en]

We consider partitioned scheduling of an Imprecise Mixed-Criticality (IMC) taskset on a uniform multiprocessor platform, with Earliest Deadline First-Virtual Deadline (EDF-VD) as the uniprocessor task scheduling algorithm, and address the optimization problem of finding a feasible task-to-processor assignment and low-criticality (LO) mode processor speed with the objective of minimizing the system’s average energy consumption in LO mode. We propose a task-to-processor assignment algorithm Criticality-Unaware Worst-Fit Decreasing (CU-WFD) algorithm, which allocates tasks with the Worst-Fit Decreasing (WFD) heuristic method based on utilization values at their respective criticality levels. We determine the energy-efficient speed for each processor based on EDF-VD scheduling, and present our algorithm Energy-Efficient Partitioned Scheduling for Imprecise Mixed-Criticality (EEPSIMC) with the CU-WFD heuristic algorithm to minimize system energy consumption. The experimental results show that our proposed algorithm has good performance in terms both schedulability ratio and normalized energy consumption compared to seven comparison baselines.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 1-1
Keywords [en]
EDF-VD, Energy consumption, Energy efficiency, energy-aware scheduling, Heuristic algorithms, Imprecise mixed-criticality, Job shop scheduling, multiprocessor, partitioned scheduling, Partitioning algorithms, Switches, Task analysis
National Category
Computer Engineering Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-213599DOI: 10.1109/TCAD.2023.3246926Scopus ID: 2-s2.0-85149061429OAI: oai:DiVA.org:umu-213599DiVA, id: diva2:1792477
Available from: 2023-08-29 Created: 2023-08-29 Last updated: 2023-10-13Bibliographically approved

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Gu, Zonghua

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CiteExportLink to record
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  • apa
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