Response time predictions for workload on new server architectures can enhance Service Level Agreement-based resource management. This paper evaluates two performance prediction methods using a distributed enterprise application benchmark. The historical method makes predictions by extrapolating from previously gathered performance data, while the layered queuing method makes predictions by solving layered queuing networks. The methods are evaluated in terms of: the systems that can be modelled; the metrics that can be predicted; the ease with which the models can be created and the level of expertise required; the overheads of recalibrating a model; and the delay when evaluating a prediction. The paper also investigates how a prediction-enhanced resource management algorithm can be tuned so as to compensate for predictive inaccuracy and balance the costs of SLA violations and server usage.
|Number of pages||19|
|Journal||Journal of Supercomputing|
|Publication status||Published - Nov 2005|
Bibliographical noteFunding Information:
The authors would like to thank Robert Berry and Beth Hutchison (IBM Hursley), Te-Kai Liu (IBM T.J. Watson Research Centre) and Nigel Thomas (University of Newcastle) for their contributions towards this research. The work is sponsored in part by the EPSRC (contract no. GR/S03058/01 and GR/R47424/01), the NASA AMES Research Center administered by USARDSG (contract no. N68171-01-C-9012) and IBM UK Ltd.
- Distributed enterprise application
- Historical performance data
- Layered queuing modelling
- Performance prediction
- Resource management
- Service level agreement
ASJC Scopus subject areas
- Theoretical Computer Science
- Information Systems
- Hardware and Architecture