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In the stochastic and dynamic edge-cloud collaborative environment, the computing resources of the host are limited, and the resource requirements of computing tasks are random and changeable. Therefore, how to efficiently schedule dynamic tasks and improve system performance becomes challenging. The scheduling algorithm based on deep reinforcement learning optimizes the delay and energy consumption of the system by dynamically interacting with the environment, which can solve the problem of dynamic and changeable environment to a certain extent, but there are still problems such as poor model adaptability, low training efficiency, and unbalanced system load. In this paper, aiming at optimizing the average response time of task scheduling and the average energy consumption of the system, a multi-objective task scheduling model is designed, and a task scheduling policy optimization algorithm based on improved asynchronous advantage actor-critic (A3C) is proposed. The residual convolutional neural network (RCNN) improves the network structure of A3C, using asynchronous multi-threaded training methods to interact with the edge-cloud collaborative environment and capturing the random dynamic characteristics of resources required for computing tasks and heterogeneous edge-cloud hosts resource change characteristics to better adapt to random dynamic environments, and it can quickly updating network parameters, improving training speed, and make full use of host resources to solve system load imbalance problems. Simulation results show that the scheduling algorithm proposed in this paper can effectively reduce task response time and system energy consumption in an edge-cloud collaborative environment. [ABSTRACT FROM AUTHOR]

System logs are an important data source for performance monitoring and anomaly detection. Analyzing logs for anomaly detection can improve service quality. At present, although machine learning algorithms for anomaly detection can achieve high accuracy, they lack robustness. The detection model cannot dynamically adapt to changes of logs when system logs contain noises owing to the casualness of the operators or log templates update. In face of this challenge, the paper proposes a robust log anomaly detection method based on contrastive learning and multi-scale Masked Sequence to Sequence (MASS). First, a log feature extraction model integrating the BERT model with contrastive learning is proposed. It can extract effective features by pulling two related normal logs together and pushing apart normal and abnormal logs to ensure that the semantic similarity between normal and abnormal log templates is lower than that between normal log templates, effectively remove abnormal log templates and distinguish log categories to which normal log templates and normal noise log templates belong rather than rudely treating the noise log templates as anomalies, which enhances the robustness of anomaly detection. Then, a multi-scale Masked Sequence to Sequence (MSMASS) model is proposed, the Attention mechanism of the MASS model is replaced with multi-scale Attention to fully learn the context information of different scales of the log sequence, which improves the accuracy of anomaly detection. Contrast experiments are conducted with four baseline methods on common datasets, and the results show that the method proposed in this paper is superior to most existing log-based anomaly detection methods in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Existing Web services recommendation models suffer from matrix sparsity and cold-start problems due to the limited number of user-service interactions and too little contextual information, so this paper introduces knowledge graph as auxiliary information to Web service recommendation to alleviate matrix sparsity and cold-start problems. And we propose the Web services recommendation based on Metapath-guided Graph Attention Network Model (WSR-MGAT) to fully exploit the structural information of the knowledge graph to improve the recommendation accuracy. Specifically, WSR-MGAT uses graph embedding method to obtain the initial embedding of entities and relationships. In order to obtain more closely related neighbors, we propose to use a distance-aware path sampling method to extract meta-path instances with closer relationships. Previous knowledge graph-based Web service recommendations do not make full use of the rich interaction information, which may lead to limited performance. To address this problem, this paper uses meta-paths to guide nodes to recursively aggregate higher-order neighbor information and use an attention mechanism to distinguish the importance of neighbors. Meanwhile, the semantic information of nodes under different meta-paths is fused to obtain a more comprehensive embedding of nodes. To verify the performance of the model, we use the real data crawled from the ProgrammableWeb platform to conduct multiple groups of experiments. The experimental results show that the WSR-MGAT improves over the strongest baselines w.r.t. Prec@10 by 4.6%; Recall@10 by 3.8%; NDCG@10 by 4.2% and F1@10 by 4.9%. [ABSTRACT FROM AUTHOR]