Abstract
Online performance monitoring of computer systems 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. Compressive sampling-based schemes can help reduce these costs on the local machine by acquiring data directly from the system in a compressed form, and in a computationally efficient way. This paper focuses on reducing the computational cost associated with recovering the original signal from the transmitted sample set at the monitoring station for anomaly detection. Towards this end, we show that the compressed samples preserve, in an approximate form, properties such as mean, variance, as well as correlation between data points in the original full-length signal. We then use this result to detect changes in the original signal that could be indicative of an underlying anomaly such as abrupt changes in magnitude and gradual trends without the need to recover the full-length data. We illustrate the usefulness of our approach via case studies involving IBM's Trade Performance Benchmark using signals from the disk and memory subsystems. Experiments indicate that abrupt changes can be detected using a compressed sample size of 25% with a hit rate of 95% for a fixed false alarm rate of 5%; trends can be detected within a confidence interval of 95% using a sample size of only 6%.