Geographic Clustering Based Mobile Edge Computing Resource Allocation Optimization Mechanism

With the development of Internet of Things (IoT), a large number of terminals and devices are connected to the network. Mobile edge computing (MEC) is proposed to assist cloud computing, to relieve the pressure of network and satisfy the requirements of delay-sensitive applications. Considering reasonable allocation of computing resources is the most important aspect corresponding to delay, this paper designs geographic clustering and collaborative scheduling (GC-CS) mechanism. This mechanism can be divided into two parts, which are the decentralized deployment of MEC servers and the resource allocation optimization in MEC. For the first part, this paper designs the load balancing based geographic clustering (LBGC) algorithm which combines the idea of greedy algorithm to realize the initial allocation of computing resources. For the second part, delay minimization oriented collaborative scheduling (DMCS) algorithm is designed to decrease the response delay without increasing system overhead. Finally, the effectiveness of the mechanism is verified by simulation in the IoT scene.