Online Optimal Service Selection, Resource Allocation and Task Offloading for Multi-Access Edge Computing: A Utility-Based Approach

Abstract

Multi-access edge computing promises satisfactory user experience by offloading tasks to the MEC server deployed at the network edge. However, since the MEC server is often resource-limited as compared to the cloud infrastructure, how to efficiently utilize its resources for system performance optimization becomes a challenge. In this paper, we study this problem with the aim at maximizing user's QoE through jointly optimizing service selection, computation resource allocation and task offloading decision, which is less studied in existing literature. We formulate a mixed-integer nonlinear programming problem (MINLP) for the task and propose a utility-based approach together with a low-complexity resource-efficiency based heuristic to address the problem. We consider realistic settings, where centralized solutions may not apply and an optimal mechanism needs to adapt as system operates. A distributed algorithm based on the Lagrangian-dual based decomposition theory is proposed, and we prove all sub-problems derived can be efficiently solved. In line with the current VM technology, we develop a cost-aware online algorithm that explicitly incorporates the cost of service switches into service selection and resource allocation. We evaluate our mechanism through both synthetic and trace-driven simulations, and results indicate they are effective as compared to representative baseline algorithms.