QoE-Aware Task Executions on Service Models in DT-Assisted Edge Computing

Mobile Edge Computing (MEC) shifts the computing power to the edge of core networks and provides important impetus in the flourishment of delay sensitive services at the network edge. Digital Twin (DT) technique enables object behavior monitoring, analysis, and prediction through data analytics and artificial intelligence, which facilitates inference service provisioning based on machine learning models. In this paper, we deal with the Quality-of-Experience (QoE) issue of user satisfaction on inference services in DT-assisted MEC networks, through executing user tasks locally or offloaded to the MEC network. We formulate two novel optimization problems: the utility maximization problem, and the dynamic utility maximization problem, with the aim to maximize the total utility of user task executions in terms of QoEs and service delays of users with the services. We first provide an Integer Linear Programming solution for the utility maximization problem when the problem size is small or medium; otherwise we devise a randomized algorithm with high probability, at the expense of bounded resource violations. We then develop an efficient online heuristic for the dynamic utility maximization problem. We also devise an online algorithm with a provable competitive ratio for a special case of the dynamic utility maximization problem without the bandwidth constraint. We finally evaluate the performance of proposed algorithms through simulations. The simulation results show that the proposed algorithms are promising.