A blockchain integration to support failures prediction from log files in multi-agent systems technology

Abstract

Computer system failures can cause significant financial and reputational damage, making it critical to detect and prevent them before they occur. In this article, we propose a novel approach for predicting system failures based on log file analysis, called MABFailPred. Our approach leverages a Blockchain-based Multi-Agent architecture to analyze execution traces and detect early warning signals for system failure prediction during execution. Moreover, we introduce a new method for extracting log keys called HCLPars. The MABFailPred approach comprises four agents that work together to gather log keys, categorize data, develop a failure model, and make predictions. The use of blockchain ensures that agent and administrator predictions are synchronized, and agent collaboration is coordinated. We validate our approach by applying it to a real-world distributed system and accurately predicting problems before they occur. Our proposed approach represents a significant advancement in the field of system failure prediction, with the potential to enhance the reliability and security of computer systems. By employing a machine learning algorithm, our approach can predict anomalies in real-time and places a strong emphasis on the continuous monitoring of system behavior. The paper includes a validation of the approach using the Hadoop Distributed File System (HDFS) log dataset. According to our findings, the proposed approach achieves superior performance compared to other methods on the same dataset, with a precision of 0.998. This result establishes a new state-of-the-art performance level for the dataset.