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首页> 外文期刊>IEEE systems journal >DLME: Distributed Log Mining Using Ensemble Learning for Fault Prediction
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DLME: Distributed Log Mining Using Ensemble Learning for Fault Prediction

机译：DLME：使用集成学习进行故障预测的分布式日志挖掘

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摘要

Fault prediction problems in network systems are often manifested as very onerous for better network management. One of the effective measures is to constantly monitor and analyze the unceasing generation of network logs that capture the activities of a network. The learning algorithms are quite useful for this purpose. However, due to the dynamic nature of network systems, a frequent drift in the logged data may occur which in turn affects the efficiency of the learning algorithms. In this paper, we present a general purpose algorithmic framework for developing easily parallelizable distributed log mining approach, which uses machine learning and distributed processing to achieve a better quality of network services. Our proposed approach monotonously handles the dynamic nature of network logs by tracking the changes in the distribution of logs and takes adequate actions according to that. The entire problem is illustrated as a distributed learning environment, where the complete set of logs is partitioned into assorted data chunks and a distributed weighted ensemble of the information is generated from these chunks. Furthermore, our method is tested on real dataset and experimental analysis shows that a fair amount of scalability and accuracy can be obtained.
机译：网络系统中的故障预测问题通常表现为对更好的网络管理非常繁重。一种有效的措施是不断监视和分析不断捕获网络活动的网络日志的生成。为此目的，学习算法非常有用。但是，由于网络系统的动态性质，可能会在记录的数据中出现频繁的漂移，进而影响学习算法的效率。在本文中，我们提出了一种通用算法框架，用于开发易于并行化的分布式日志挖掘方法，该方法使用机器学习和分布式处理来获得更好的网络服务质量。我们提出的方法通过跟踪日志分布的变化单调处理网络日志的动态性质，并据此采取适当的措施。整个问题被说明为分布式学习环境，其中完整的日志集被划分为各种数据块，并从这些块中生成信息的分布式加权集合。此外，我们的方法在真实数据集上进行了测试，实验分析表明，可以获得相当数量的可伸缩性和准确性。

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