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 ...
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.