边缘场景下面向分布式交互应用的服务器分配

Edge server assignment for distributed interactive applications in edge environments

移动边缘计算作为一种极具前瞻性的分布式计算范式,将云计算的计算能力下沉到网络边缘来高效地处理数据。近年来,分布式交互应用的需求激增,移动智能设备数量爆炸性增长,作为移动边缘计算的重要组成部分,边缘服务器可以使交互应用程序在用户附近执行,从而解决通信和网络开销过大和数据无法即时处理的问题。一个关键的挑战是找到一个合适的边缘服务器分配策略以有效降低交互延迟和平衡服务器工作负载。基于此目标提出了边缘服务器分配算法ESADQN,将问题建模为马尔可夫决策过程,使用强化学习有效地选择边缘服务器部署位置,并将用户分配到相应服务器。与k-means算法相比,ESADQN算法在工作负载标准差相近的情况下,总交互时延平均减少了31%;与Top-K算法相比,ESADQN算法在总交互时延相近的情况下,工作负载标准差平均减少了49%。实验结果表明,ESADQN选择的服务器分配方案能有效降低交互时延和工作负载标准差。

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.