Robust clustering analysis for the management of self-monitoring distributed systems

Authors:
Andres Quiroz;Nathan Gnanasambandam;Manish Parashar;Naveen Sharma
Affiliations:
The Applied Software Systems Laboratory, Rutgers University, Piscataway, USA;Xerox Research Center Webster (XRCW), Webster, USA;The Applied Software Systems Laboratory, Rutgers University, Piscataway, USA;Xerox Research Center Webster (XRCW), Webster, USA
Venue:
Cluster Computing
Year:
2009

Citing 18
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Abstract

We present a decentralized algorithm for online clustering analysis used for anomaly detection in self-monitoring distributed systems. In particular, we demonstrate the monitoring of a network of printing devices that can perform the analysis without the use of external computing resources (i.e. in-network analysis). We also show how to ensure the robustness of the algorithm, in terms of anomaly detection accuracy, in the face of failures of the network infrastructure on which the algorithm runs. Further, we evaluate the tradeoff in terms of overhead necessary for ensuring this robustness and present a method to reduce this overhead while maintaining the detection accuracy of the algorithm.