Joint time delay and energy optimization with intelligent overclocking in edge computing

Abstract

With the rapid growth of user equipment (UE), the amount of data transmitted over networks has become enormous, exerting immense pressure on backbone networks and central cloud infrastructures. Simultaneously, corresponding applications requiring high energy consumption and low latency have multiplied the requirements for UE. Mobile edge computing (MEC) has been proposed to support the offloading of UE tasks to edge clouds for execution. The implementation of MEC requires fast data transmission between UE and edge servers, and the emerging 5G network appears to render this technology possible. In this paper, considering a large number of UE, a fixed MEC server, and an advanced intelligent network, we suggest an intelligent overclocking mechanism for the MEC server that operates for an intelligently calculated period to allow it to leverage more computing power without introducing additional hardware resources for a certain period of time. We jointly manage task offloading, server resource allocation, and overclocking to minimize the system-wide computation overhead and other risks. The proposed optimization problem is a mixed-integer nonlinear programming problem that is divided into three subproblems: offloading decision, resource allocation, and overclocking decision. We solve these subproblems using non-convex techniques and provide an iterative algorithm to obtain a heuristic solution for the original problem. Finally, simulation results show that the overclocked MEC server has lower system-wide computation overhead, faster task processing, and more offloaded UE as compared with the case without overclocking.