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.

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 %.

The growing availability of the Internet provides opportunities for depression screening. In recent years, depression analysis based on social media texts has shown great promise, yet most works have focused on treating it as a binary problem. Meanwhile, existing methods need the perception of fine-grained emotions in historical contexts. Moreover, they do not consider the time interval between posts and thus ignore the decay of emotions over time. In this paper, we propose a Time-aware Depression identification model based on Emotion capturing (TIDE), a novel method to identify the severity of depression in social media users. We define the assessment as an ordinal regression problem to distinguish differences in depression levels. Specifically, TIDE uses Plutchik's wheel of emotions to characterize the emotional historical spectrum, then models the emotion decay process of historical context using a Time-aware LSTM (T-LSTM). We experiment on two Reddit public datasets to demonstrate that our approach outperforms state-of-the-art models. Then, ablation studies and qualitative analysis further demonstrate the validity of the proposed modules. Overall, our work can facilitate social media-based analysis of depression and shows potential for application to mental health-related issues.