Energy Efficiency Based Joint Computation Offloading and Resource Allocation 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. By offloading the latency-critical or computation-intensive tasks to the edge server, mobile devices (MDs) could save energy consumption and extend battery life. However, unlike cloud servers, resource bottlenecks in MEC servers limit the scalability of offloading. Hence, computation offloading and resource allocation need to be optimized. 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 non-linear programming (MINLP) problem. First, we design a bound improving branch-and-bound (BnB) algorithm to find the global optimal solution. Then, we present a combinational algorithm to obtain the suboptimal solution for practical application. Simulation results reveal that the combinational algorithm performs very closely to the BnB algorithm in energy saving, but it has a better performance in average algorithm time. Furthermore, our proposed solutions outperform other benchmark schemes.