Utility-Aware Edge Server Deployment in Mobile Edge Computing

Traditional Mobile Cloud Computing (MCC) has gradually turned to Mobile Edge Computing (MEC) to meet the needs of low-latency scenarios. However, due to the unpredictability of user behaviors, how to arrange edge servers in suitable locations and rationally allocate the computing resources is not easy. Besides, the workload between the servers maybe unbalanced, which could lead to a shrinkage of system utility and waste of energy. So we analyze the workloads in a large MEC system and use one day to represent a workload cycle rotation. Combining the idea of differential workload changes with the local greedy method, we propose a new Gradient algorithm under the constraint of given limited computing capacity. We conduct extensive simulations and compared it with the algorithm based on the average workload as the Weight and the Greedy algorithm, which shows that the Gradient algorithm can reach the maximum utility compared with Weight and Greedy methods.