Distributed mobile cloud computing: Joint optimization of radio and computational resources

We consider a scenario composed by multiple mobile users asking for computation offloading of their applications to a set of cloud servers. A set of radio access points, small cell base stations possibly coexisting with macro base stations, are available to provide radio proximity access to fixed computational resources. Our objective is to find the optimal assignment of each mobile user to a cloud server through the most convenient base station and, contextually, the optimal MIMO precoding matrices and computational rates (virtual machines) to each user, under latency constraints dictated by the users Quality of Experience (QoE). The radio resources assigned to users belonging to the same cell are orthogonal to each other, whereas users of different cells might interfere against each other. The latency constraint imposes a strict relationship between the time spent for transferring the program execution from the mobile device to the fixed server (and viceversa) and the time needed to execute the computation. To properly exploit this relationship, we formulate the computation offloading problem as a joint optimization of the radio and computational resources, with the objective of minimizing the overall energy consumption, at the mobile terminal side, while meeting the latency constraints. The resulting optimization problem is nonconvex in both the objective function and in the constraints. Nevertheless, by hinging on successive convex approximation techniques, we propose an iterative algorithm able to converge to a local optimal solution of the original nonconvex problem.