Mobile Edge Server Deployment towards Task Offloading in Mobile Edge Computing: A Clustering Approach

Recent years have witnessed the effect of Mobile Edge Computing (MEC) during resource-intensive and time-critical applications toward various mobile devices. Therefore, Mobile Edge Severs (MES) are widely deployed adjacent to the 5G Base Station (BS) to upgrade the performance of the specific application system. Unfortunately, there have rare researches for the location planning of edge servers in the MEC scenario. The deployment of MES may cover a wide range of theoretical concerns, such as computation offloading cost, system performance. In this paper, we consider the problem of optimization of MES deployment in multiple BSs scenarios. To achieve this, we proposed an approach based on the improved K-Means clustering to determine the theoretical location and amount of edge servers. Besides, mobile computation tasks are strategically assigned to the distance-first edge server. To this end, we then develop a reasonable deployment scheme based on K-means for edge servers, which can effectively reduce the network delay, energy consumption, and cost of edge servers. We have compared the density-based clustering algorithm proposed in the recent research. Extensive simulation results indicate that our strategy reduces average completion time by 15.7 % , power consumption by 22 % , and overhead by 19 % in edge server deployment issues.