UG环球:Power Optimization Based on Dynamic Content Refresh in Mobile Edge Computing

新2备用网址/2020-06-21/ 分类:科技/阅读:

跟着移动互联网的飞速成长与相干技能的不绝晋升,社交类应用已成为现下主流应用之一.同时,手机应用的成果也越来越富厚,其能耗需求以及信息处理赏罚手段也越来越大.针对移动社交平台忽略收集状况、频仍革新内容(笔墨、图片、视频等)造成的高能耗以及运算手段题目,提出一种边沿计较模式下基于Markov决定进程(Markov decision process,MDP)的能耗优化模子.该模子思量差异环境的收集状况,按照手机当前电量以及用户革新频率,通过当地移动边沿计较层完成数据处理赏罚,在Markov决定进程天生的决定表中选择最优计策,动态选择最佳的收集接入以及革新下载最佳的图片名目.该模子不只镌汰革新时刻,并且可以或许低落移动平台的能耗.尝试功效表白:对比于行使单一收集的图片革新模式,在担保不减罕用户革新次数的条件下,该能耗优化模子低落能耗约12.1%.

Abstract:

Nowadays,with the rapid development of mobile Internet and related technologies,social applications have become one of the mainstream applications.At the same time,the functions of mobile applications are also getting richer and richer,and their energy consumption requirements and information processing capabilities are also growing.In view of the problem of high energy consumption and computing power caused by mobile social platforms ignoring network status and frequently refreshing content (words,pictures,videos,etc.),a consumption optimization model based on Markov decision process (MDP) in edge computing is proposed.The model considers the network status in different environments and performs data processing through the local edge computing layer (simulating the local edge computing mode and completing data processing) according to the current power of the mobile phone and the user refresh rate.It selects optimal strategy from the decision tables generated by the Markov decision process,and dynamically selects the best network access and refreshes the best download picture format.The model not only reduces refresh time,but also reduces the power consumption of the mobile platform.The experimental results show that compared with the picture refresh mode using a single network,the energy consumption optimization model proposed in this paper reduces the energy consumption by about 12.1% without reducing the number of user refresh cycles.

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