文章基本信息

标题：Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms
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作者：Manal A. El Sayed ; El Sayed M. Saad ; Rasha F. Aly 等
期刊名称：Computers
电子版ISSN：2073-431X
出版年度：2021
卷号：10
期号：1
页码：10
DOI：10.3390/computers10010010
出版社：MDPI Publishing
摘要：Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability.
关键词：dynamic voltage/frequency scaling; energy-aware partitioning; multi-core real-time systems; shared resources; task allocating and scheduling dynamic voltage/frequency scaling ; energy-aware partitioning ; multi-core real-time systems ; shared resources ; task allocating and scheduling