QoE-DEER: A QoE-Aware Decentralized Resource Allocation Scheme for Edge Computing

Abstract

With the increasing prevalence of online services mounted on IoT devices, edge computing gains significant momentum over conventional cloud-centric architecture. Edge servers are geographically deployed in a distributed manner nearby IoT devices, which not only frees online services from the high hardware requirement but also sharply reduces network latency experienced by IoT users. Recent works have extensively studied the problem of edge resource management and request scheduling to achieve high Quality of Service (QoS) with low latency, but there has been little focus on Quality of Experience (QoE) that an edge resource allocation scheme brings about. In this article, we study the Edge Resource Allocation (ERA) problem across multiple service requests with the objective of overall QoE maximization, which has non-polynomial computational complexity. To attack the $\mathcal {NP}$ -hardness of solving the ERA problem, we adopt a game-theoretic approach to formulate the ERA problem as a potential game ERAGame which admits a Nash Equilibrium (NE). Then, we novelly present a decentralized algorithm namely QoE-DEER to find an NE solution which equivalently maximizes the overall QoE as the ERA problem. Finally, the performance and convergence of our algorithm is evaluated both theoretically and experimentally, which indicates its significant advantages over the state-of-the-art approaches.