System-centric energy efficient computation offloading and resource allocation in latency-sensitive MEC systems

Mobile edge computing (MEC) enables computation tasks of end users to be offloaded and processed at nearby edge servers, which is a promising technology that can effectively alleviate end users' computing pressure. However, how to balance two important metrics for MEC computation offloading, i.e., latency and energy efficiency, is still a key challenge. In this paper, we investigate joint computation offloading and resource allocation for latency-sensitive applications in order to maximize system-centric computation energy efficiency (SCEE) in MEC systems. An optimization problem is formulated to maximize the sum computation energy efficiency of all users which contains the energy consumption at both user and edge server sides. Meanwhile, task completion latency constraints for all users are considered. We decompose the original optimization problem into two tractable subproblems. The optimal computation offloading as well as communication and computing resource allocation solutions are obtained by solving these subproblems. Furthermore, a hierarchical system-centric energy efficient computation offloading and resource allocation (SEECR) algorithm is developed. Numerical results show that compared with the benchmark schemes, the proposed algorithm can effectively improve the computation energy efficiency performance while guaranteeing the latency requirements of all users.