Efficient Energy Joint Computation Offloading and Resource Optimization in Multi-Access MEC Systems

With the rapid growth of computation demands from mobile applications, mobile-edge computing (MEC) provides a new method to meet requirement of high data rate and high computation capability. Toward this end, we consider a multi-access MEC servers system in which Orthogonal Frequency-Division Multiplexing Access (OFDMA) is used as the transmission mechanism for uplink. In order to minimize energy consumption of MDs, we propose a joint optimization strategy for computation offloading, subcarrier allocation, and computing resource allocation, which is a mixed integer nonlinear programming (MINLP) problem. Because of the mutual constraints between variables, it is difficult to decompose this problem into sub-problems. Thus, we design a variable fusion based branch-and-bound algorithm (VF-BnB) to find the optimal solution. Simulation results reveal that our proposed solution not only achieves better performance in energy saving than the conventional schemes but also performs well in successful offloading probability (SOP).