Evaluating compressive sampling strategies for performance monitoring of data centers

ABSTRACT

Performance monitoring of data centers provides vital information for dynamic resource provisioning, fault diagnosis, and capacity planning decisions. Online monitoring, however, incurs a variety of costs---the very act of monitoring a system interferes with its performance, and if the information is transmitted to a monitoring station for analysis and logging, this consumes network bandwidth and disk space. This paper proposes a low-cost monitoring solution using compressive sampling---a technique that allows certain classes of signals to be recovered from the original measurements using far fewer samples than traditional approaches---and evaluates its ability to measure typical parameters or signals generated in a data-center setting using a testbed comprising the Trade6 enterprise application. Experiments indicate that by using the compressive sampling mechanism, the recovered signal adequately preserves the spikes and other abrupt changes present in the original. The results, therefore, open up the possibility of using low-cost compressive sampling techniques to detect performance bottlenecks and anomalies in data centers that manifest themselves as abrupt changes exceeding operator-defined threshold values in the underlying signals.

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Index Terms

• Computing methodologies

  • Modeling and simulation

    • Model development and analysis

      • Modeling methodologies

• General and reference

  • Cross-computing tools and techniques

    • Design