Tracking time-varying parameters in software systems with extended Kalman filters
CASCON '05 Proceedings of the 2005 conference of the Centre for Advanced Studies on Collaborative research
A performance analysis method for autonomic computing systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
The Future of Software Performance Engineering
FOSE '07 2007 Future of Software Engineering
Implementing Adaptive Performance Management in Server Applications
SEAMS '07 Proceedings of the 2007 International Workshop on Software Engineering for Adaptive and Self-Managing Systems
Real-time performance modeling for adaptive software systems
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
Integrated estimation and tracking of performance model parameters with autoregressive trends
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Tracking adaptive performance models using dynamic clustering of user classes
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Autonomic load-testing framework
Proceedings of the 8th ACM international conference on Autonomic computing
Mitigating DoS Attacks Using Performance Model-Driven Adaptive Algorithms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Indirect estimation of service demands in the presence of structural changes
Performance Evaluation
| Hi-index | 0.00 |
Autonomic computer systems react to changes in the system, including failures, load changes, and changed user behaviour. Autonomic control may be based on a performance model of the system and the software, which implies that the model should track changes in the system. A substantial theory of optimal tracking filters has a successful history of application to track parameters while integrating data from a variety of sources, an issue which is also relevant in performance modeling. This work applies Extended Kalman Filtering to track the parameters of a simple queueing network model, in response to a step change in the parameters. The response of the filter is affected by the way performance measurements are taken, and by the observability of the parameters.