TY - GEN
T1 - Online automated reliability classification of queueing models for streaming processing using support vector machines
AU - Beard, Jonathan C.
AU - Epstein, Cooper
AU - Chamberlain, Roger D.
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2015.
PY - 2015
Y1 - 2015
N2 - When do you trust a performance model? More specifically, when can a particular model be used for a specific application? Once a stochastic model is selected, its parameters must be determined. This involves instrumentation, data collection, and finally interpretation; which are very time consuming. Even when done correctly, the results hold for only the conditions under which the system was characterized. For modern, dynamic stream processing systems, this is far too slow if a model-based approach to performance tuning is to be considered. This work demonstrates the use of a Support Vector Machine (SVM) to determine if a stochastic queueing model is usable or not for a particular queueing station within a streaming application. When combined with methods for online service rate approximation, our SVM approach can select models while the application is executing (online). The method is tested on a variety of hardware and software platforms. The technique is shown to be highly effective for determining the applicability of M/M/1 and M/D/1 queueing models to stream processing applications.
AB - When do you trust a performance model? More specifically, when can a particular model be used for a specific application? Once a stochastic model is selected, its parameters must be determined. This involves instrumentation, data collection, and finally interpretation; which are very time consuming. Even when done correctly, the results hold for only the conditions under which the system was characterized. For modern, dynamic stream processing systems, this is far too slow if a model-based approach to performance tuning is to be considered. This work demonstrates the use of a Support Vector Machine (SVM) to determine if a stochastic queueing model is usable or not for a particular queueing station within a streaming application. When combined with methods for online service rate approximation, our SVM approach can select models while the application is executing (online). The method is tested on a variety of hardware and software platforms. The technique is shown to be highly effective for determining the applicability of M/M/1 and M/D/1 queueing models to stream processing applications.
UR - https://www.scopus.com/pages/publications/84944030236
U2 - 10.1007/978-3-662-48096-0_7
DO - 10.1007/978-3-662-48096-0_7
M3 - Conference contribution
AN - SCOPUS:84944030236
SN - 9783662480953
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 82
EP - 93
BT - Euro-Par 2015
A2 - Traff, Jesper Larsson
A2 - Hunold, Sascha
A2 - Versaci, Francesco
PB - Springer Verlag
T2 - 21st International Conference on Parallel and Distributed Computing, Euro-Par 2015
Y2 - 24 August 2015 through 28 August 2015
ER -