2012 RainMonAnIntegratedApproachtoMi

• (Shafer et al., 2012) ⇒ Ilari Shafer, Kai Ren, Vishnu Naresh Boddeti, Yoshihisa Abe, Gregory R. Ganger, and Christos Faloutsos. (2012). “RainMon: An Integrated Approach to Mining Bursty Timeseries Monitoring Data.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339711

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+RainMon%3A+An+Integrated+Approach+to+Mining+Bursty+Timeseries+Monitoring+Data
• http://dl.acm.org/citation.cfm?id=2339530.2339711&preflayout=flat#citedby

Quotes

Author Keywords

• Bursty data; data mining; pca; performance and usage measurement; system monitoring

Abstract

Metrics like disk activity and network traffic 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 effectively, 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 RainMonAnIntegratedApproachtoMi	Christos Faloutsos Ilari Shafer Kai Ren Vishnu Naresh Boddeti Yoshihisa Abe Gregory R. Ganger			RainMon: An Integrated Approach to Mining Bursty Timeseries Monitoring Data				10.1145/2339530.2339711		2012