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ISTC-CC Abstract
RainMon: An Integrated Approach to Mining
Bursty Timeseries Monitoring Data
18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'12), August 2012.
Ilari Shafer, Kai Ren, Vishnu Naresh Boddeti, Yoshihisa Abe, Gregory R. Ganger,
Christos Faloutsos
Carnegie Mellon University
Metrics like disk activity and network trac are widespread sources of diagnosis and monitoring information in datacenters and networks. However, as the scale of these systems increases, examining the raw data yields diminishing insight. We present RainMon, a novel end-to-end approach for mining timeseries monitoring data designed to handle its size and unique characteristics. Our system is able to (a) mine large, bursty, real-world monitoring data, (b) find significant trends and anomalies in the data, (c) compress the raw data eectively, and (d) estimate trends to make forecasts. Furthermore, RainMon integrates the full analysis process from data storage to the user interface to provide accessible long-term diagnosis. We apply RainMon to three real-world datasets from production systems and show its utility in discovering anomalous machines and time periods.
KEYWORDS: System Monitoring, PCA, Bursty Data
FULL PAPER: pdf