Achieving Energy Efficiency Through Dynamic Computing Offloading in Mobile Edge-Clouds

TLDR: This paper proposes a dynamic computing offloading (DCL) problem, which aims to minimize the maximum energy consumption of the mobile devices with constraints on computation tasks latency in a Mobile Edge-Computing (MEC) network.

Abstract: There is a fundamental and critical problem in modern mobile applications, in which the battery life of mobile devices is usually limited. Recently, some researchers prolong the life of batteries by offloading computation tasks to edge-servers which are deployed near the mobile devices. However, computing offloading causes extra delay, which may severely downgrade the user experience especially for the delay-sensitive applications. Moreover, the dynamic nature of mobile devices and the limited computation capacity of edge-servers also bring another challenges for tradeoff optimization between energy consumption and task completion latency. In this paper, we propose a dynamic computing offloading (DCL) problem, which aims to minimize the maximum energy consumption of the mobile devices with constraints on computation tasks latency in a Mobile Edge-Computing (MEC) network. To solve the problem, we consider two complementary cases: offline case (we sacrifice response time to achieve better service results) and online case (where we have to make immediate offloading decision for each computation task arrived online). For the offline case, we propose an efficient RMCL algorithm, and prove that our RMCL method achieves at least O((log m)/α + 1) of the optimum with high probability, where m is the number of computation tasks in a time slot, and α is a value depending on the minimum edge-server capacity and the maximum computation task demand, with α ≥ 1 under most practical situations. For the online case, we propose an algorithm, named OMCL, which considers a trade off between the latency and energy consumption. The performance of our proposed algorithms is evaluated by formal analysis and simulation on a small-scale system. The simulation results show that the algorithm can reduce the maximum energy consumption in a set of mobile devices by 40% compared with executing computation tasks locally.