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非平稳多任务下动态功耗管理

马喜强,刘维亚,郑喜凤,程鹏   

  1. 中国科学院长春光学精密机械与物理研究所
  • 收稿日期:2012-12-21 修回日期:2013-03-12 发布日期:2013-06-20
  • 通讯作者: 马喜强

Dynamic Power Management for Non-stationary Multi-task

  • Received:2012-12-21 Revised:2013-03-12 Published:2013-06-20
  • Contact: Xi-Qiang MA

摘要: 传统的嵌入式系统动态功耗管理策略仅从设备的角度考察工作负载状况,忽略了工作负载的应用特征,不能同时指出决策时刻和转换状态,针对这一问题,本文从系统状态的角度分析负载,提出非平稳多任务下功耗策略改进方法——SMBSP(System message based stochastic policy)。首先,介绍了现有的动态功耗管理策略算法,同时阐述了算法需要改进的原因。然后,基于系统信息建立了任务的设备利用率统计查找表,并根据实际的间隔时间更新分布。最后,建立了半Markov随机模型,定义了代价函数和目标优化函数,并给出了平均准则下基于线性规划的求取最优策略方法。实验结果表明,在考虑性能约束的条件下,该算法具有很好的稳定性;延迟率小于0.10;竞争率可以达到0.57。更稳定更有效地降低了功耗,有利于在嵌入式系统中应用。

关键词: 动态功耗管理, Markov决策过程, 策略优化, 嵌入式系统

Abstract: Traditional dynamic power management policies of embedded system only focus on equipment workload situation from the perspective of device, and ignore the application features of the workload. An improved method called SMBSP (System message based stochastic policy) for non-stationary multi-task is proposed on the basis of analyzing workload according to system state. First, this paper introduces the dynamic power management strategy algorithm, and the reason why it needs to be improved is expounded. Then, device-requester utilization lookup table was constructed based on system message, and updated distribution according to the actual time interval. Finally, a Semi-Markov stochastic model was built, the discount criteria optimization method based on linear programming strategy was given, the moment of decision and conversion state are pointed out. Experimental results show that, while considering the conditions of the performance constraints, the algorithm has good stability; delay rate is less than 0.10; competitive rates reached 0.57. The algorithm reduces power consumption more efficiently than other policies, and is beneficial to the application in embedded systems.

Key words: Dynamic power management, Markov decision processes, Policy optimization, Embedded system

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