A review of optimization methods for computation offloading in edge computing networks

Handling the massive amount of data generated by Smart Mobile Devices (SMDs) is a challenging computational problem. Edge Computing is an emerging computation paradigm that is employed to conquer this problem. It can bring computation power closer to the end devices to reduce their computation latency and energy consumption. Therefore, this paradigm increases the computational ability of SMDs by collaboration with edge servers. This is achieved by computation offloading from the mobile devices to the edge nodes or servers. However, not all applications benefit from computation offloading, which is only suitable for certain types of tasks. Task properties, SMD capability, wireless channel state, and other factors must be counted when making computation offloading decisions. Hence, optimization methods are important tools in scheduling computation offloading tasks in Edge Computing networks. In this paper, we review six types of optimization methods -they are Lyapunov optimi-zation, convex optimization, heuristic techniques, game theory, machine learning, and others. For each type, we focus on the objective functions, application areas, types of offloading methods, evaluation methods, as well as the time complexity of the proposed algorithms. We discuss a few research problems that are still open. Our purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks.

Automated summary

Edge Computing is an emerging paradigm that brings computation power closer to end devices, reducing latency and energy consumption. Optimization methods play a crucial role in scheduling computation offloading tasks in Edge Computing networks.