Two-timescale online learning of joint user association and resource scheduling in dynamic mobile edge computing

For the mobile edge computing network consisting of multiple base stations and resource-constrained user devices, network cost in terms of energy and delay will incur during task offoading from the user to the edge server. With the limitations imposed on transmission capacity, computing resource, and connection capacity, the perslot online learning algorithm is first proposed to minimize the time-averaged network cost. In particular, by leveraging the theories of stochastic gradient descent and minimum cost maximum flow, the user association is jointly optimized with resource scheduling in each time slot. The theoretical analysis proves that the proposed approach can achieve asymptotic optimality without any prior knowledge of the network environment. Moreover, to alleviate the high network overhead incurred during user handover and task migration, a two-timescale optimization approach is proposed to avoid frequent changes in user association. With user association executed on a large time scale and the resource scheduling decided on the single time slot, the a symptotic optimality is preserved. Simulation results verify the effectiveness of the proposed online learning algorithms.