Research Article
Enhanced Inferencing: Estimation of a Workload Dependent Performance Model
@INPROCEEDINGS{10.4108/ICST.VALUETOOLS2009.7799, author={Dinesh Kumar and Li Zhang and Asser Tantawi}, title={Enhanced Inferencing: Estimation of a Workload Dependent Performance Model}, proceedings={4th International ICST Conference on Performance Evaluation Methodologies and Tools}, publisher={ICST}, proceedings_a={VALUETOOLS}, year={2010}, month={5}, keywords={service time CPU overhead performance prediction}, doi={10.4108/ICST.VALUETOOLS2009.7799} }- Dinesh Kumar
Li Zhang
Asser Tantawi
Year: 2010
Enhanced Inferencing: Estimation of a Workload Dependent Performance Model
VALUETOOLS
ICST
DOI: 10.4108/ICST.VALUETOOLS2009.7799
Abstract
Performance modeling of software systems is vital for predictive analysis of their performance and capacity planning of the host environment. Robust performance prediction and effcient capacity planning highly depend on an accurate estimation of the underlying model parameters. AMBIENCE, which is a prototype tool developed at IBM Research, makes use of the powerful Inferencing technique to generate a workload-independent parameters based performance model. However, modern software systems are quite complex in design and may exhibit variable service times and overheads at changing workloads. In this work, we extend the Inferencing technique for generating workload-dependent service time and CPU overhead based performance models. We call this extended form as Enhanced Inferencing. Implementation of Enhanced Inferencing in AMBIENCE shows signifcant improvement of the order of 26 times over Inferencing. We further present a case study where Enhanced Inferencing provides a quantitative performance difference between consolidated and partitioned software system installations. Ability to carry out such evaluations can have signifcant impact on capacity planning of software systems that are characterized by workload-dependent model parameters.