Paper Info
Title
Self-correlating predictive information tracking for large-scale production systems
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).
Year
DOI
Venue
2009
10.1145/1555228.1555235
ICAC
Keywords
Field
DocType
large-scale production system,real system trace,managed system,continuous monitoring service,continuous monitoring cost,remote information update,predictive information tracking system,system monitoring,infotrack system,high information precision,distributed system,management system,production system
PlanetLab,Spatial correlation,Computer science,Tracking system,Real-time computing,Continuous monitoring,Cluster analysis,Distributed computing,Scalability
Conference
Citations 
PageRank 
References 
6
0.53
20
Authors
5
Name
Order
Citations
PageRank
Ying Zhao190249.19
Yongmin Tan21697.58
Zhenhuan Gong335113.71
Xiaohui Gu41975103.57
Mike Wamboldt560.53