Execution Latency and Energy Consumption Tradeoff in Mobile-Edge Computing Systems

In this paper, we consider a mobile-edge computing (MEC) system consisting of an MEC server and multiple mobile devices, in which each mobile device executes computation tasks with the help of the MEC server. We aim at minimizing the weighted sum of the execution latency and energy consumption by jointly allocating the CPU-cycles, the transmission power and bandwidth, as well as computation offloading decisions. To address this issue, we first decompose the original optimization problem into several subproblems by using Lagrange dual decomposition, which can be operated by mobile devices in a distributed manner. Although the MEC execution resource allocation subproblem is non-convex, we show that it can be decomposed into a two-stage optimization problem to derive the optimal solution by using Karush-Kuhn-Tucker conditions and exact line search algorithm. Moreover, it is shown that the internal problem can be equivalently transformed into a convex one, and the external problem is a single-variable optimization problem. Simulation results are presented to show that, there exists an inherent tradeoff between the execution latency and energy consumption of mobile devices, and the computation task can be processed with less energy consumption if certain execution latency is tolerable.