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We consider a mobile cloud computing system consisting of multiple users, one computing access point (CAP), and one remote cloud server. The CAP can either process the received tasks from mobile users or offload them to the cloud. We aim to jointly optimize the offloading decisions of all users and the CAP, together with communication and processing resource allocation, to minimize the overall cost of energy, computation, and the maximum delay among all users. It is shown that the problem can be formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. We further propose an efficient solution to this problem 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.
Chen et al. (Tue,) studied this question.
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