FOQL: Software Aging Determination and Rejuvenation Strategy Generation for Docker

• Published in: Proceedings : annual International Computer Software and Applications Conference pp. 1268 - 1273
• Main Authors: Liu, Yiming, Liu, Zhuanzhuan, Tan, Xueyong, Liu, Jing
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02.07.2024
• Subjects: Accuracy; Aging; aging state determination; Computer bugs; Docker; FS-OWA algorithm; Q-learning; reinforcement learning; rejuvenation generation; Software; Software algorithms; System performance
• ISSN: 2836-3795
• DOI: 10.1109/COMPSAC61105.2024.00167

As a platform for creating, deploying, and managing containers, Docker has long been tasked with handling high workloads, making it highly susceptible to aging-related bugs. As these bugs accumulate, the system may exhibit anomalies such as increased resource utilization, task scheduling failures, and response time delays. At this juncture, the system is subject to software aging. If left unresolved, this problem may escalate to more severe consequences such as system crashes and downtime, significantly diminishing the availability and reliability of the system. In order to address the software aging and restore system performance, it has become an urgent problem to accurately determine the aging state of the Docker platform and generate targeted rejuvenation operations reasonably and effectively. Therefore, this paper proposes a synthesis method for determining the aging state and generating rejuvenation operations, named FOQL. Firstly, the FS-OWA algorithm is employed to analyze resource usage according to the varying degrees of aging states, accurately determining whether the system is in an aging state. Secondly, if the system enters an aging state, the Q- Learning algorithm evaluates the value of each rejuvenation operation based on the degree of aging and the cost of rejuvenation operations (such as downtime), ultimately generating the optimal operation. Finally, the experimental results show that, in determining the aging state, the recognition accuracy of the FS-OWA algorithm reached 99.3%, surpassing baseline algorithms by up to 16.52 %. In generating rejuvenation operations, Q-learning algorithm generates a Q-table containing the value of each state-action pair. Based on this table, the optimal rejuvenation operation can be selected for execution. In conclusion, the utilization of the FOQL method effectively mitigates the aging problem and ensures the service quality of the system.