Digital twin-driven intelligent task offloading for collaborative mobile edge computing

Abstract

Collaborative mobile edge computing (MEC) is a new paradigm that allows cooperative peer offloading among distributed MEC servers to balance their computing workloads. However, the highly dynamic workloads and wireless network conditions pose great challenges to achieving efficient task offloading in collaborative MEC. To address this challenge, digital twin (DT) has emerged as one promising solution by building a high-fidelity virtual mirror of the physical MEC to simulate its behaviors and help make optimal operational decisions. In this paper, we propose a DT-driven intelligent task offloading framework for collaborative MEC, where DT is employed to map the collaborative MEC system into a virtual space and optimize the task offloading decisions. We model the task offloading process as a Markov decision process (MDP) with the objective of maximizing the MEC system’s total income from providing computing services, and then develop a deep reinforcement learning (DRL)-based intelligent task offloading scheme (INTO) to jointly optimize the peer offloading and resource allocation decisions. An efficient action refinement method is proposed to ensure that the action selected by the DRL agent is feasible. Experimental results show that our proposed approach can effectively adapt the task offloading decisions according to the dynamic environment, and significantly improve the MEC system’s income through extensive comparison with three state-of-the-art algorithms.