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The log analysis-based system fault diagnosis method can help engineers analyze the fault events generated by the system. The K-means algorithm can perform log analysis well and does not require a lot of prior knowledge, but the K-means-based system fault diagnosis method needs to be improved in both efficiency and accuracy. To solve this problem, we propose a system fault diagnosis method based on a reclustering algorithm. First, we propose a log vectorization method based on the PV-DM language model to obtain low-dimensional log vectors which can provide effective data support for the subsequent fault diagnosis; then, we improve the K-means algorithm and make the effect of K-means algorithm based log clustering; finally, we propose a reclustering method based on keywords’ extraction to improve the accuracy of fault diagnosis. We use system log data generated by two supercomputers to verify our method. The experimental results show that compared with the traditional K-means method, our method can improve the accuracy of fault diagnosis while ensuring the efficiency of fault diagnosis.

Logs that record system abnormal states (anomaly logs) can be regarded as outliers, and the k-Nearest Neighbor (kNN) algorithm has relatively high accuracy in outlier detection methods. Therefore, we use the kNN algorithm to detect anomalies in the log data. However, there are some problems when using the kNN algorithm to detect anomalies, three of which are: excessive vector dimension leads to inefficient kNN algorithm, unlabeled log data cannot support the kNN algorithm, and the imbalance of the number of log data distorts the classification decision of kNN algorithm. In order to solve these three problems, we propose an efficient log anomaly detection method based on an improved kNN algorithm with an automatically labeled sample set. This method first proposes a log parsing method based on N-gram and frequent pattern mining (FPM) method, which reduces the dimension of the log vector converted with Term frequency.Inverse Document Frequency (TF-IDF) technology. Then we use clustering and self-training method to get labeled log data sample set from historical logs automatically. Finally, we improve the kNN algorithm using average weighting technology, which improves the accuracy of the kNN algorithm on unbalanced samples. The method in this article is validated on six log datasets with different types.

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When SaaS software suffers from the problem of response time degradation, scaling deployment resources that support the operation of software can improve the response time, but that also means an increase in the costs due to additional deployment resources. For the purpose of saving costs of deployment resources while improving response time, scaling out the SaaS software is an alternative approach. However, how scaling out software affects response time in the case of saving deployment resources is an important issue for effectively improving response time. Therefore, in this paper, we propose a method analysing the impact of scaling out software on response time. Specifically, we define the scaling-out operation of SaaS software and then leverage queueing theory to analyse the impact of the scaling-out operation on response time. According to the conclusions of impact analysis, we further derive an algorithm for improving response time based on scaling out software without using additional deployment resources. Finally, the effectiveness of the analysis’s conclusions and the proposed algorithm is validated by a practical case, which indicates that the conclusions of impact analysis obtained from this paper can play a guiding role in scaling out software and improving response time effectively while saving deployment resources.