Self-correlating predictive information tracking for large-scale production systems

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 ex...

• Type: Conference
• Year: 2009
• Where: ICAC
• Authors: Ying Zhao, Yongmin Tan, Zhenhuan Gong, Xiaohui Gu, Mike Wamboldt
• Keywords: Autonomic Computing, Continuous Monitoring, Continuous Monitoring Cost, ICAC 2009, Large-scale Production Systems