RainMon: An Integrated Approach to Mining Bursty Timeseries Monitoring Data

Ilari Shafer, Kai Ren, Vishnu Naresh Boddeti, Yoshihisa Abe, Gregory R. Ganger and Christos Faloutos

People

Ilari Shafer

Kai Ren

Vishnu Naresh Boddeti

Yoshihisa Abe

Gregory R. Ganger

Christos Faloutos

Data

Code

Overview

Timeseries data are prevalent in large-scale computing centers. Systems often capture sampled metrics of performance, utilization, and even sensor data like temperature. These streams are used for monitoring, placement, optimization, and more. RainMon is a framework to manage massive data-center timeseries streams that are lengthy and bursty in nature. It uses a multi-stage modeling approach. In the first phase, the incoming data streams are decomposed into “smooth” and “spiky” components. In the second phase, the streams are summarized into a set that can be visualized and understood. In the third phase, predictions are made about the future state of the system. Such a framework provides the potential to address a number of practical advances for data center efficiency,

  • Detecting potential anomalies or alert conditions. Dramatic changes in model parameters can be predictors of problems or abnormalities.

  • Reduce the storage requirement of streams through compression. By storing only the model parameters and occasional original data points, potentially large timeseries data can be effectively summarized.

  • Improve alerting and placement algorithms by predicting future usage. Even a short-term view of future usage can be valuable for decision-making. By playing the model forward, we can obtain this data.

The framework incorporates several existing algorithms from the literature including Cypress, SPIRIT and Kalman filters. RainMon has been applied to large data streams collected from production clusters to detect real anomalies.

References

RainMon: An Integrated Approach to Mining Bursty Timeseries Monitoring Data

Ilari Shafer, Kai Ren, Vishnu Naresh Boddeti, Yoshihisa Abe, Gregory R. Ganger and Christos Faloutos
18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2012 (oral)