A New Statistical Method for Anomaly Detection in Distributed Systems

Distributed computing systems are increasing in popularity and being widely used as a new way of large-scale data processing. However, to achieve a reliable and efficient performance in a distributed environment, it is important to deal with system anomalies as soon as they are encountered. In this paper, two novel anomaly detection algorithms will be introduced and compared with previous anomaly detection algorithms. These novel algorithms are devised based on data summarization and error prediction in comparison with previously extracted data. The result of our experiments show that the proposed methods exhibit higher performance in terms of precision and accuracy.