Task Offloading in Cloud-Edge Collaborative Environment Based on Deep Reinforcement Learning and Fuzzy Logic
Xiao‐Jun Wu, Lulu Wang, Sheng Yuan, Wei Chai · 2024
Cloud-edge collaborative computing is a key technology towards delay-sensitive and computation-intensive applications in future cellular networks. This paper considers the combination of edge computing and cloud computing, and studies the problem of task offloading in an “End-Edge-Cloud” collaborative architecture optimized for Quality of Experience (QoE) and system stability. To this end, this paper proposes a dual experience pool task offloading algorithm based on deep reinforcement learning and fuzzy logic (FDRL-DEP). Considering dynamic offloading requests and time-varying communication conditions, this paper models the problem as a Markov process and applys Dueling Deep Q-Network (Dueling DQN) to implement it. And this paper designs a two-stage fuzzy logic controller to improve the instability of Dueling DQN to the system when the parameters are not optimized. Via extensive simulation and theoretical analyses, this paper shows the effectiveness of the proposed FDRL-DEP framework on improving QoE and system stability.