A game theoretic resource allocation for overall energy minimization in mobile cloud computing system

Cloud computing and virtualization techniques provide mobile devices with battery energy saving opportunities by allowing them to offload computation and execute code remotely. When the cloud infrastructure consists of heterogeneous servers, the mapping between mobile devices and servers plays an important role in determining the energy dissipation on both sides. From an environmental impact perspective, any energy dissipation related to computation should be counted. To achieve energy sustainability, it is important reducing the overall energy consumption of the mobile systems and the cloud infrastructure. Furthermore, reducing cloud energy consumption can potentially reduce the cost of mobile cloud users because the pricing model of cloud services is pay-by-usage. In this paper, we propose a game-theoretic approach to optimize the overall energy in a mobile cloud computing system. We formulate the energy minimization problem as a congestion game, where each mobile device is a player and his strategy is to select one of the servers to offload the computation while minimizing the overall energy consumption. We prove that the Nash equilibrium always exists in this game and propose an efficient algorithm that could achieve the Nash equilibrium in polynomial time. Experimental results show that our approach is able to reduce the total energy of mobile devices and servers compared to a random approach and an approach which only tries to reduce mobile devices alone.