Power Optimization Based on Dynamic Content Refresh in Mobile Edge Computing

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.