DQN-Based Collaborative Computation Offloading for Edge Load Balancing
Zihui Li, Ke Yu, Hao Zhou, Xiaofei Wu · 2023
The emergence of mobile edge computing (MEC) supports large-scale computing demand in the Internet of Things era by moving computing pressure from cloud center to the edge. However, computing resources of edge servers are scattered and restricted. A carefully designed computation offloading mechanism is the key to MEC. Besides, it is necessary for cloud centers and edge servers to participate in computation together. Most of the existing researches focus only on a single computation architecture to offload computing tasks and neglect the impact of load balancing on offloading efficiency. In this paper, we consider a cloud-edge-device collaborative computing scenario, leveraging the advantages of different computing levels. In order to direct computing task to service node with sufficient computing resources and avoid edge servers overloading or being idle all the time, we introduce an occupancy table to dynamically track the edge load status. Then we propose a deep Q-network (DQN) based collaborative computation offloading (DQNCCO) method for edge load balancing, jointly minimizing delay, energy consumption and edge load balancing. Experimental results show that the proposed method can balance the use of the whole edge computing resources while realizing suitable fine-grained offloading decisions.