A semidefinite relaxation approach to mobile cloud offloading with computing access point

Meng-Hsi Chen, Ben Liang, Min Dong

Abstract: We consider a mobile cloud computing scenario consisting of one user with multiple independent tasks, one computing access point (CAP), and one remote cloud server. The CAP can either process the received tasks from the mobile user or offload them to the cloud, providing additional computation capability over traditional mobile cloud computing systems. We aim to optimize the offloading decision of the user to minimize the overall cost of energy, computation, and delay. It is shown that the problem can be formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. We propose an efficient offloading decision algorithm by semidefinite relaxation and a novel randomization mapping method. Our simulation results show that the proposed algorithm gives nearly optimal performance with only a small number of randomization iterations, and adding CAPs to the traditional dichotomy of mobile devices and remote cloud servers can drastically improve mobile cloud computing performance.