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.%随着移动互联网的飞速发展与相关技术的不断提升,社交类应用已成为现下主流应用之一.同时,手机应用的功能也越来越丰富,其能耗需求以及信息处理能力也越来越大.针对移动社交平台忽略网络状态、频繁刷新内容(文字、图片、视频等)造成的高能耗以及运算能力问题,提出一种边缘计算模式下基于Markov决策过程(Markov decision process,MDP)的能耗优化模型.该模型考虑不同环境的网络状态,根据手机当前电量以及用户刷新频率,通过本地移动边缘计算层完成数据处理,在Markov决策过程生成的决策表中选择最优策略,动态选择最佳的网络接入以及刷新下载最佳的图片格式.该模型不仅减少刷新时间,而且能够降低移动平台的能耗.实验结果表明:相比于使用单一网络的图片刷新模式,在保证不减少用户刷新次数的前提下,该能耗优化模型降低能耗约12.1％.
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