Evaluating compressive sampling strategies for performance monitoring of data centers
Proceedings of the 9th international conference on Autonomic computing
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Real-time performance analysis of processor behaviourrequires the efficient gathering of micro-architecturalinformation from processor cores. Such information can beexpected to be highly structured allowing it to be compressed, but the computational burden of conventional compression techniques exclude their use in this environment. We consider the use of new mathematical techniques that allow a signal to be compressed and recovered from a relatively small number of samples. These techniques, collectively termed Compressive Sampling,are asymmetric in that compression is simple, but recovery is complex. This makes them appropriate for applications in which the simplicity of the sensor can be offset against complexity at the ultimate recipient of the sensed information. We evaluate the practicality of using such techniques in the transfer of signals representing one or more micro-architectural counters from a processor core. We show that compressive sampling is usable to recover such performance signals, evaluating the trade-off between efficiency, accuracy and practicability within its variousvariants.