Computation Offloading in Heterogeneous Mobile Edge Computing With Energy Harvesting
Tian Zhang, Wei Chen
Abstract
Energy harvesting aided mobile edge computing (MEC) has gained much attention for its widespread application in the computation-intensive, latency-sensitive and energy-hungry scenario. Computation offloading, which leverages powerful MEC servers (MEC-ss) to augment the computing capability of less powerful mobile devices (MDs), is intrinsically a distributed computing over heterogeneous MEC networks. In this article, computation offloading from multi-MD to multi-MEC-s in heterogeneous MEC systems with energy harvesting is investigated from a game theoretic perspective. The objective is to minimize the average response time of an MD that consists of communication time, waiting time and processing time. M/G/1 queueing models are established for MDs' computation generation and MEC-ss' computation task receiving. The interference among MDs, the randomness in computation task generation, harvested energy arrival, wireless channel state, queueing at the MEC-s, and the power budget constraint of an MD are taken into consideration. A noncooperative computation offloading game is formulated. The action is a vector that denotes the amount of computation tasks offloaded to all MEC-ss (the element value can be zero) and local process. We give the definition and existence analysis of the Nash equilibrium (NE). Furthermore, we reconstruct the optimization problem of an MD. A 2-step decomposition is presented and performed. Thereby, we arrive at a one-dimensional search problem and a greatly shrunken sub-problem. The sub-problem is nonconvex, but its Karush-Kuhn-Tucker (KKT) conditions have finite solutions. We can obtain the optimal solution of the sub-problem by seeking the finite solutions. Thereafter, a distributive NE-orienting iterated best-response algorithm is designed. Simulations are carried out to illustrate the convergence performance and effectiveness of the proposed algorithm.