An Online Mirror-Prox Optimization Approach to Proactive Resource Allocation in MEC

In this paper, we consider a multi-access edge computing (MEC) network with one base station (BS) and multiple users. A number of edge computing servers with limited computing and storage capability are attached to the BS to execute the computation tasks offloaded by the users. We develop an online mirror-prox optimization (OMO) algorithm to minimize the overall network delay for task computation. Solving the underlying optimization problem distributively across the users and over a long time horizon to obtain globally optimal decisions at individual users is challenging due to having to cope with time-varying cost function and constraints with unknown statistics. To make the proposed algorithm perform in a distributed and globally optimal manner at users, the BS broadcasts information based on the current states of the servers to individual users. We evaluate the performance of the algorithm using two performance metrics which are dynamic regret, assessing the closeness of the achievable cost against the dynamic optimal value, and aggregate violation, measuring the asymptotic satisfaction of the constraints. The simulation results indicate the effectiveness of the proposed algorithm in the long term and achieve considerable efficiency improvement in lower battery consumption at users' devices.