Distributed Multiuser Computation Offloading for Cloudlet-Based Mobile Cloud Computing: A Game-Theoretic Machine Learning Approach

Abstract

In this paper, we investigate the problem of multiuser computation offloading for cloudlet-based mobile cloud computing in a multichannel wireless contention environment. The studied system is fully distributed so that each mobile device user can make the offloading decisions based only on its individual information, and without information exchange. We first formulate this multiuser computation offloading decision making problem as a noncooperative game. After analyzing the structural property of the formulated game, we show that it is an exact potential game, and has at least one pure-strategy Nash equilibrium point (NEP). To achieve the NEPs in a fully distributed environment, we propose a fully distributed computation offloading (FDCO) algorithm based on machine learning technology. We then theoretically analyze the performance of the proposed FDCO algorithm in terms of the number of beneficial cloudlet computing mobile devices and the system-wide execution cost. Finally, simulation results validate the effectiveness of our proposed algorithm compared with counterparts.