A New Statistical Method for Anomaly Detection in Distributed Systems

被引：1
|
作者：
Vafaie, Bamdad [1 ]
Shamsi, Mahboubeh [2 ]
Javan, Morteza Sargolzaei [3 ]
El-Khatib, Khalil [1 ]
机构：
[1] Ontario Tech Univ, Fac Business & Informat Technol, Oshawa, ON, Canada
[2] Qom Univ Technol, Fac Elect & Comp Engn, Qom, Iran
[3] Amirkabir Univ Technol, Dept Comp Engn & IT, Tehran, Iran
来源：
2020 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) | 2020年
关键词：
Distributed systems; Distributed Monitoring; Anomalies in Distributed systems; Statistical analytic techniques; the Principal component analysis; DIAGNOSIS;
D O I：
10.1109/ccece47787.2020.9255700
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
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页数：4
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