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Self-Correlating Predictive Information Tracking for Large-Scale Production Systems (2009)
| Venue: | in Proc. of ICAC |
| Citations: | 14 - 5 self |
BibTeX
@INPROCEEDINGS{Zhao09self-correlatingpredictive,
author = {Ying Zhao and Yongmin Tan and Zhenhuan Gong and Xiaohui Gu and Mike Wamboldt},
title = {Self-Correlating Predictive Information Tracking for Large-Scale Production Systems},
booktitle = {in Proc. of ICAC},
year = {2009}
}
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Abstract
Automatic management of large-scale production systems requires a continuous monitoring service to keep track of the states of the managed system. However, it is challenging to achieve both scalability and high information precision while continuously monitoring a large amount of distributed and time-varying metrics in large-scale production systems. In this paper, we present a new self-correlating, predictive information tracking system called InfoTrack, which employs lightweight temporal and spatial correlation discovery methods to minimize continuous monitoring cost. InfoTrack combines both metric value prediction within individual nodes and adaptive clustering among distributed nodes to suppress remote information update in distributed system monitoring. We have implemented a prototype of the InfoTrack system and deployed the system on the PlanetLab. We evaluated the performance of the InfoTrack system using both real system traces and micro-benchmark prototype experiments. The experimental results show that InfoTrack can reduce the continuous monitoring cost by 50-90 % while maintaining high information precision (i.e., within 0.01-0.05 error bound).
Keyphrases
large-scale production system self-correlating predictive information tracking continuous monitoring cost infotrack system high information precision real system trace automatic management distributed system monitoring predictive information time-varying metric remote information update new self-correlating adaptive clustering managed system metric value prediction continuous monitoring service distributed node micro-benchmark prototype experiment individual node spatial correlation discovery method large amount experimental result error bound
