Determine When and How to Perform Edge Rejuvenation Effectively for Cloud-Edge Collaborative System

The Cloud-Edge collaborative system, which combines the advantages of cloud computing and edge computing, has become the preferred architecture for large-scale distributed systems. However, prolonged high-load operation of Cloud-Edge collaborative system may result in software aging, significantly impacting the reliability of the Cloud-Edge collaborative system, especially in resource-constrained edge environments. Proactive rejuvenation can help restore system robustness, but it comes with costs and affects normal system operation. Determining the appropriate rejuvenation timing and implementing an efficient rejuvenation strategy are crucial. Since the reliability of the edge system is closely related to the stable operation of the entire Cloud-Edge collaborative system, this paper proposes a comprehensive rejuvenation model named SM-OLR. This model calculates the rejuvenation time for the edge system and performs the rejuvenation operation, which is divided into two stages. The first stage involves determining the rejuvenation timing. A Semi-Markov model is used to represent the system state. Specific distribution functions are fitted based on measured values to accurately describe the system's state transitions. This enables a more scientific modeling of the system's state and ensures precise calculation of rejuvenation timing. The second stage focuses on the rejuvenation strategy. In the Cloud-Edge collaborative environment, interactions between edge and cloud, and edge and edge are easily facilitated, making task offloading a highly effective rejuvenation method. The paper adopts task offloading as the rejuvenation strategy. The reinforcement learning algorithm SARSA is employed to dynamically decide the offloading decision for specific tasks. Experiments were conducted in KubeEdge, a representative Cloud-Edge system. The results demonstrate that SM-OLR incurs lower rejuvenation overhead compared to traditional reboot rejuvenation, and the edge system can continue to provide services during the rejuvenation process. By performing effective rejuvenation operations, the performance of the edge system can be improved by 57 %.