PLUFS: 一种开销敏感的周期任务在线多处理器节能实时调度算法

张冬松; 王珏; 赵志峰; 吴飞

doi:10.7544/issn1000-1239.2016.20160163

PLUFS: 一种开销敏感的周期任务在线多处理器节能实时调度算法

¹(镇江船艇学院江苏镇江 212001)
²(上海工程技术大学电子电气工程学院上海 201620) (dszhang@nudt.edu.cn)

基金项目: 国家自然科学基金项目(61402527,61272097)

详细信息

中图分类号: TP316.4
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出版历程
- 发布日期: 2016-06-30

PLUFS: An Overhead-Aware Online Energy-Efficient Scheduling Algorithm for Periodic Real-Time Tasks in Multiprocessor Systems

¹(Zhenjiang Watercraft College, Zhenjiang, Jiangsu 212001)
²(College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620)

摘要

摘要: 现有周期任务多处理器节能调度算法虽然在考虑处理器实际开销情况下可以实现较好的节能效果，但仍不能保证最优可调度性.针对嵌入式实时系统中不可忽视的状态切换开销，提出一种开销敏感的周期任务在线多处理器节能实时调度算法PLUFS.该算法通过TL面流调度模型与处理器实际切换开销模型相结合，在每个TL面的初始时刻、任务结束执行时刻实现节能调度，在不违反周期任务集最优可调度性的前提下，达到实时约束与能耗节余的合理折中.经过理论证明和模拟实验，结果表明：PLUFS算法不仅保证了周期任务集的最优可调度性，而且节能效果整体优于现有算法，能耗节余比现有算法提高约10%～20%.
- 开销 /
- 多处理器系统 /
- 节能 /
- 周期任务 /
- 实时系统
Abstract: Although some existing multiprocessor energy-efficient approaches for periodic real-time tasks can achieve more energy savings with taking practical overhead of processor into consideration, they cannot guarantee the optimal feasibility of periodic tasks. For the non-ignorable overhead of switching the processor state in embedded real-time systems, this paper proposes an overhead-aware online energy-efficient real-time scheduling algorithm in multiprocessor systems, the periodic tasks with largest utilization first based on switching overhead (PLUFS). PLUFS utilizes the fluid scheduling concept of time local (TL) remaining execution plane and the switching overhead of the processor states to implement energy-efficient scheduling for real-time tasks in multiprocessors at the initial time of each TL plane as well as at the end execution time of a periodic task in each TL plane. Consequently, PLUFS can obtain a reasonable tradeoff between the real-time constraint and the energy-saving while realizing the optimal feasibility of periodic tasks. Mathematical proof and extensive simulation results demonstrate that PLUFS guarantees the optimal feasibility of periodic tasks, and on average saves more energy than existing algorithms, and improves the saved energy of some existing algorithms by about 10% to 20% at the same time.
- overhead /
- multiprocessor systems /
- energy-efficient scheduling /
- periodic task /
- real-time system

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