Joint User Association and Value-Aware Computation Offloading for MEC-Enabled Networks

Abstract

Computation offloading in mobile edge computing (MEC) plays an important role in mitigating the burden on users. However, there remain some obstacles hindering the full use of the benefits of computation offloading in MEC-enabled networks. Besides, the intensive deployment of access points (APs) integrated with MEC servers (MESs) leads to a user association dilemma, which makes the offloading decision and resource allocation more challenging. In this paper, we investigate the optimization problem of user association, offloading decision, and resource allocation for MEC-enabled networks. Different from the existing works that always make utility optimization from users' perspective, an optimization problem is formulated in this paper, which aims to improve the potential benefits from the operator's perspective under the constraints of users' quality of service (QoS). Moreover, we present a value-aware scheme to classify tasks so that the operator can make reasonable pricing for different performance requirements of tasks. Due to the non-convex property of the above problem, we propose an iterative framework to decouple the original problem into two sub-problems, namely a user association problem and a joint offloading decision and resource allocation problem. These two problems are solved by matching theory and block coordinate reduction (BCD), respectively. A User Association based on Matching Theory (UAMT) algorithm and a Joint Optimization of Offloading Decision and Resource Allocation based on BCD (JOODRA-BCD) algorithm are designed correspondingly. The simulation results verify that the proposed algorithm has a fast convergence property and outperforms the benchmark algorithms in terms of the utility of the operator.