An Optimization-Based Approach to Offloading in Ad-Hoc Mobile Clouds

Abstract

An ad-hoc mobile cloud allows a mobile user to access a potential cloud resource with a short offloading latency and a low bandwidth consumption from nearby mobile devices, namely mobile cloudlets via short-range communications. However, due to the user and cloudlet mobility, the time-varying wireless channel properties, and the cloudlet's limited computation resource, designing an effective offloading algorithm involves challenges in workload distribution, cost estimation and energy minimization. In this paper, to address these challenges, we develop an optimization-based offloading algorithm that enables the mobile user to make an optimal offloading decision. The proposed algorithm takes into account effects of the user's workload, the diverse connectivity of cloudlets, and the wireless environment on the offloading action. More specifically, we formulate and solve a Markov decision process (MDP) scheme to achieve an optimal offloading policy for the mobile user with the objective of maximizing the user's utility while minimizing the offloading cost. Extensive simulations were performed to evaluate the performance of the proposed MDP scheme. The simulation results show that the proposed scheme outperforms baseline schemes.