Quality of experience aware task execution in digital twinning vehicular edge computing: A framework and A3C algorithm

Abstract

Real-time computationally intensive task scheduling for intelligent transportation system (ITS) applications like road safety and traffic forecasting within the deadline while ensuring user quality of experience (QoE) is a complex engineering problem. Meanwhile, adopting Digital Twin (DT) as an emerging technology in vehicular edge computing (VEC) enables efficient capture of real-time state information, thereby addressing the resource scheduling problem in an unpredictable vehicular topology setting. However, exploring strategies to enhance user QoE in timeliness and reliability domains could be a compelling and underexplored research challenge, particularly within the dynamic and trust-sensitive context of vehicular edge computing. In this paper, we have developed an optimization framework using Mixed Integer Linear Programming (MILP), which maximizes user QoE by allocating task execution responsibility to highly reliable and reputed vehicles in a DT-enabled VEC environment. The framework leverages the demand-supply theory of economics to cluster vehicles based on computational resources and applies multi-weighted subjective logic to ensure accurate reputation updates. The NP-hard nature of the formulated optimization problem has driven us to develop an Asynchronous Advantage Actor-Critic (A3C)-based deep reinforcement learning algorithm, namely DARQoE, for offloading tasks in the Internet of Vehicles (IoV). The developed DARQoE framework utilizes effective parallelization across multiple agents with separate environments, accelerating the learning process for IoV task offloading. The experimental results of the developed DARQoE framework demonstrate significant performance improvements in terms of QoE in the timeliness and reliability domains of task execution by up to 15 % and 25 %, respectively, compared to state-of-the-art works.