Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
httperf—a tool for measuring web server performance
ACM SIGMETRICS Performance Evaluation Review
On the Applicability of Compressive Sampling in Fine Grained Processor Performance Monitoring
ICECCS '09 Proceedings of the 2009 14th IEEE International Conference on Engineering of Complex Computer Systems
Spatio-temporal compressive sensing and internet traffic matrices
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Automated anomaly detection and performance modeling of enterprise applications
ACM Transactions on Computer Systems (TOCS)
Monalytics: online monitoring and analytics for managing large scale data centers
Proceedings of the 7th international conference on Autonomic computing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
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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.