动态电源管理的在线优化技术  

Online optimization of dynamic power management

作　　者：翟剑锋 李衍杰[1] 陈浩耀[1] ZHAI Jian-feng;LI Yan-jie;CHEN Hao-yao(Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen Guangdong 518055, China)

机构地区：[1]哈尔滨工业大学深圳研究生院,广东深圳518055

出　　处：《控制理论与应用》2018年第1期65-70,共6页Control Theory & Applications

基　　金：国家自然科学基金项目(61004036);深圳市基础计划项目(JCYJ20150731105106111)资助~~

摘　　要：研究离散事件动态系统中的一类随机离散动态系统—–半Markov决策过程,在动态电源管理问题中的应用.动态电源管理问题存在于很多便携式电子设备中,其主要目的是根据电子设备的状态通过电源管理策略选择关闭或休眠一些元器件,从而实现节省电子设备功耗,延长电池使用时间的目的.首先讨论了动态电源管理问题的建模,给出了一种带有禁止时间的在线优化方法,该方法通过设备自身运行数据,自主地学习并改进电源的动态管理策略,从而使每台电子设备具有个性化的动态电源管理方式,其优化过程可以在设备充电时完成,不需要通过云传输和云计算,避免了隐私数据的泄漏.最后通过仿真实验验证了算法的有效性.This paper mainly considers the application of Semi-Markov decision process(SMDP),which is a stochastic discrete event dynamic system(DEDS),in the dynamic power management(DPM)problems.There are the DPM problemsin many portable devices,which can provide satisfactory performance with minimum power consumption by selectively turning off system components,which enter the idle state,to save the energy consumption and prolong the service time.Firstly,the SMDP model of DPM is introduced and then an online optimization approach with forbidden time is presented.This approach can learn to improve the policy of DPM by analyzing the data from the device itself and make the device have an individual power management policy.The optimization can be implemented during the charging and thus does not need the cloud transportation and computation,which avoids the leak of private data.Finally,a simulation example is applied to illustrate the applicability of the approach.

关 键 词：动态电源管理 智能节能 半MARKOV决策过程 在线优化 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]