Research Article
Real-Time Performance Modeling for Adaptive Software Systems
@INPROCEEDINGS{10.4108/ICST.VALUETOOLS2009.7944, author={Dinesh Kumar and Asser Tantawi and Li Zhang}, title={Real-Time Performance Modeling for Adaptive Software Systems}, proceedings={1st International ICST Workshop on Run-time mOdels for Self-managing Systems and Applications}, publisher={ACM}, proceedings_a={ROSSA}, year={2010}, month={5}, keywords={estimation}, doi={10.4108/ICST.VALUETOOLS2009.7944} }- Dinesh Kumar
Asser Tantawi
Li Zhang
Year: 2010
Real-Time Performance Modeling for Adaptive Software Systems
ROSSA
ICST
DOI: 10.4108/ICST.VALUETOOLS2009.7944
Abstract
Modern, adaptive software systems must often adjust or reconfigure their architecture in order to respond to continuous changes in their execution environment. Efficient autonomic control in such systems is highly dependent on the accuracy of their representative performance model. In this paper, we are concerned with real-time estimation of a performance model for adaptive software systems that process multiple classes of transactional workload. Based on an open queueing network model and an Extended Kalman Filter (EKF), experiments in this work show that: 1) the model parameter estimates converge to the actual value very slowly when the variation in incoming workload is very low, 2) the estimates fail to converge quickly to the new value when there is a step-change caused by adaptive reconfiguration of the actual software parameters. We therefore propose a modified EKF design in which the measurement model is augmented with a set of constraints based on past measurement values. Experiments demonstrate the effectiveness of our approach that leads to significant improvement in convergence in the two cases.