An entropy-based algorithm for data elimination in time-driven software instrumentation
While monitoring, instrumented long running parallel applications generate huge amount of instrumentation data. Processing and storing this data incurs overhead, and perturbs the execution. A technique that eliminates unnecessary instrumentation data and lowers the intrusion without loosing any performance information is valuable for tool developers. This paper presents a new algorithm for software instrumentation to measure the amount of information content of instrumentation data to be collected. The algorithm is based on entropy concept introduced in information theory, and it makes selective data collection for a time-driven software monitoring system possible.
software instrumentation entropy parallel performance distributed parallel processing