Title :
Performance prediction in production environments
Author :
Schopf, Jennifer M. ; Berman, Francine
Author_Institution :
Dept. of Comput. Sci. & Eng., California Univ., San Diego, La Jolla, CA, USA
fDate :
30 Mar-3 Apr 1998
Abstract :
Accurate performance predictions are difficult to achieve for parallel applications executing on production distributed systems. Conventional point-valued performance parameters and prediction models are often inaccurate since they can only represent one point in a range of possible behaviors. The authors address this problem by allowing characteristic application and system data to be represented by a set of possible values and their probabilities, which they call stochastic values. They give a practical methodology for using stochastic values as parameters to adaptable performance prediction models. They demonstrate their usefulness for a distributed SOR application, showing stochastic values to be more effective than single (point) values in predicting the range of application behavior that can occur during execution in production environments
Keywords :
parallel processing; probability; software performance evaluation; adaptable performance prediction models; application data; distributed SOR application; parallel applications; performance prediction; production distributed systems; production environments; stochastic values; system data; value probabilities; Application software; Bandwidth; Computer science; Concurrent computing; Predictive models; Processor scheduling; Production systems; Resource management; Stochastic processes; Stochastic systems;
Conference_Titel :
Parallel Processing Symposium, 1998. IPPS/SPDP 1998. Proceedings of the First Merged International ... and Symposium on Parallel and Distributed Processing 1998
Conference_Location :
Orlando, FL
Print_ISBN :
0-8186-8404-6
DOI :
10.1109/IPPS.1998.669995