Service Reliability Based on Fault Prediction and Container Migration in Edge Computing

• Published in: Applied sciences Vol. 13; no. 23; p. 12865
• Main Authors: Liu, Lizhao, Kang, Longyu, Li, Xiaocui, Zhou, Zhangbing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2023
• Subjects: Algorithms; Cloud computing; container migration; Data mining; edge computing; Energy consumption; Failure; fault prediction; Internet of Things; Machine learning; Optimization; Quality of service; service reliability; Support vector machines
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app132312865

With improvements in the computing capability of edge devices and the emergence of edge computing, an increasing number of services are being deployed on the edge side, and container-based virtualization is used to deploy services to improve resource utilization. This has led to challenges in reliability because services deployed on edge nodes are pruned owing to hardware failures and a lack of technical support. To solve this reliability problem, we propose a solution based on fault prediction combined with container migration to address the service failure problem caused by node failure. This approach comprises two major steps: fault prediction and container migration. Fault prediction collects the log of services on edge nodes and uses these data to conduct time-sequence modeling. Machine-learning algorithms are chosen to predict faults on the edge. Container migration is modeled as an optimization problem. A migration node selection approach based on a genetic algorithm is proposed to determine the most suitable migration target to migrate container services on the device and ensure the reliability of the services. Simulation results show that the proposed approach can effectively predict device faults and migrate services based on the optimal container migration strategy to avoid service failures deployed on edge devices and ensure service reliability.