PAD: Performance Anomaly Detection in Multi-server Distributed Systems

2014 ◽  
Author(s):  
Manjula Peiris ◽  
James H. Hill ◽  
Jorgen Thelin ◽  
Sergey Bykov ◽  
Gabriel Kliot ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Anomaly Detection ◽  
Performance Anomaly ◽  
Multi Server

Anomaly Detection via Mining Numerical Workflow Relations from Logs

2020 ◽  
Author(s):  
Bo Zhang ◽  
Hongyu Zhang ◽  
Pablo Moscato
Keyword(s):  
Distributed Systems ◽  
Anomaly Detection ◽  
Text Messages ◽  
Distributed File System ◽  
Log Data ◽  
Sliding Windows ◽  
Novel Approach ◽  
Hadoop Distributed File System ◽  
Blue Gene ◽  
Online Anomaly Detection

<div>Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system's runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows' size and achieves fast anomaly detection.</div><div>The experimental results confirm that ADR is effective for both offline and online anomaly detection. </div>

Distributed predictive performance anomaly detection for virtualised platforms

2018 ◽  
Vol 11 (4) ◽  
pp. 279 ◽  
Author(s):  
Ali Imran Jehangiri ◽  
Ramin Yahyapour ◽  
Edwin Yaqub ◽  
Philipp Wieder
Keyword(s):  
Anomaly Detection ◽  
Predictive Performance ◽  
Performance Anomaly

scholarly journals Performance Anomaly Detection in Web Services: an RNN-based Approach Using Dynamic Quality of Service Features

2020 ◽  
Vol 64 (2) ◽  
pp. 729-752
Author(s):  
Muhammad Hasnain ◽  
Seung Ryul Jeong ◽  
Muhammad Fermi Pasha ◽  
Imran Ghani
Keyword(s):  
Quality Of Service ◽  
Web Services ◽  
Anomaly Detection ◽  
Performance Anomaly ◽  
Dynamic Quality
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