考虑概率和任务迁移的软实时系统节能调度

Ying Li, J. Niu, Xiang Long, Meikang Qiu
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引用次数: 6

摘要

采用电池供电的嵌入式实时系统面临的主要挑战是既要满足实时系统的要求,又要使能耗最小化。研究了异构多核软实时系统中DAG(有向无环图)任务的节能问题。针对软实时系统能够容忍偶尔的时间违规和任务在给定概率下在截止日期前完成的特点,本文提出了一种新的处理器和电压分配方案——自适应处理器和概率电压分配(APVAP),以实现在给定概率下满足时间约束要求的最小能耗。以往的工作大多集中在多核处理器任务分配的前后继(P-S)任务。然而,本文引入了亲和性来表示后续任务可以根据任务特征和工作负载重新分配到更合适的内核。此外,本文引入数据迁移能量(DME)的概念,计算任务迁移到不同核心时的传输能量,并采用时间与能量比(RTE)来确定最适合迁移的任务,以减少能耗和执行时间为代价。实验结果表明,我们的方法优于该领域最先进的算法(最大改进30.7%)。
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Energy efficient scheduling with probability and task migration considerations for soft real-time systems
The main challenges for embedded real-time systems which use battery as their power supply are both to satisfy the requirements of real-time systems and minimize the energy consumption. This paper studies the energy saving problem for DAG (Directed Acyclic Graph) tasks in soft real-time systems with heterogeneous multicore processors. Since soft real-time systems can tolerate occasional time violations and tasks are completed before deadlines with a given probability, this paper proposes a novel processor and voltage assignment scheme - Adaptive Processor and Voltage Assignment with Probability (APVAP) to realize the minimum energy consumption which can satisfy the requirements of time constraints under the given probability. Most of previous work focuses on multicore processor task assignment for predecessor and successor (P-S) tasks. However, this paper introduced affinity to indicate successor tasks can be re-allocated to more appropriate cores according to task features and workload. Besides, this paper introduces the concept of data migration energy (DME) to compute the transmission energy when a task is migrated to a different core and adopts Ratio between Time and Energy (RTE) to determine the most suitable tasks for migration to reduce energy consumption at the cost of execution time. The experimental results demonstrate that our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 30.7%).
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