Research On Mobile Edge Computing Server Deployment In 5G Ultra-dense Network Environment

With the emergence of compute-intensive applications and the rapid increase of mobile terminals,the centrally deployed cloud computing model can no longer satisfy the latency and reliability requirements of mobile Internet and Internet of things data processing.To alleviate the high latency and network congestion problems of the cloud computing model,the Mobile Edge Computing(MEC)model is proposed,which migrates cloud computing capabilities to the edge of the mobile network.The core of MEC technology is the deployment of MEC servers between mobile devices and cloud computing centers,which provide edge computing services for computation offloading tasks to reduce response latency and improve user experience.The deployment of edge servers plays a critical role in improving the efficiency of computing service and resource utilization,but there is still a lack of in-depth and effective research on this problem.In order to meet the rapid growth of mobile devices and mobile traffic,ultra-dense networking technology,which is one of the key technologies of 5G,has become the development trend of future wireless mobile communication networks,and the deployment of MEC servers in ultra-dense networks can better meet the future convergence of network computing and communication.Therefore,this paper focuses on the deployment of MEC servers in 5G ultra-dense network environments.By analyzing the key issues such as offloading,computing,and communication involved in the process of deploying MEC server in the ultra-dense network,the problem is formulated as an optimization problem under multiple constraints with the objective of minimizing the response time of the computing tasks.A deployment algorithm for area division based on the idea of vector quantization,a deployment algorithm for local search based on base station suitability assessment and a deployment algorithm based on an improved intelligent algorithm are proposed to solve the optimal deployment of MEC server and the optimal association between mobile users and MEC server,and compared with the typical deployment algorithms based on density,workload-based and random-based.In the experiment part,the average communication time,average queuing time and average response time obtained by the simulation system are taken as the metrics to measure the performance of the algorithms,and fully discusses the performance of different deployment algorithms in terms of benchmark experiments,different numbers of servers,different numbers of mobile users,different server service rates,different communication computation ratios and time complexity.The experimental results show that the proposed algorithms outperform the comparison algorithm in terms of optimizing system communication time,queuing time and response time.It can minimize the response time of computing tasks and achieve the optimal deployment and mobile user allocation of the MEC server.