Abstract: Mobile edge computing, as a highly forward-looking distributed computing paradigm, brings the computing power of cloud computing to the edge of the network to efficiently process data. In recent years, with the surge in demand for distributed interactive applications and the explosive growth in the number of mobile smart devices, edge servers, as a crucial component of mobile edge computing, enable interactive applications to execute close to users, thereby addressing issues of excessive communication and network overheads as well as delays in real-time data processing. A key challenge lies in finding a suitable edge server allocation strategy to effectively reduce interactive latency and balance server workloads. To this end, we propose the edge server allocation algorithm based on deep Q-network (ESADQN), which models the problem as a Markov decision process and utilizes reinforcement learning to effectively select edge server deployment locations and allocate users to corresponding servers. Compared to the k-means algorithm, ESADQN achieves an average reduction of 31% in total interactive latency with similar workload standard deviation. When compared to the Top-K algorithm, ESADQN reduces the workload standard deviation by an average of 49% with comparable total interactive latency. Experimental results demonstrate that the server allocation scheme selected by ESADQN can effectively reduce both interactive latency and workload standard deviation.

Key words: edge computing, server assignment, distributed interactive applications, Markov decision process, reinforcement learning