Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Performance Guarantees for Web Server End-Systems: A Control-Theoretical Approach
IEEE Transactions on Parallel and Distributed Systems
Adaptive Control
Differentiated Caching Services; A Control-Theoretical Approach
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
An adaptive dual control framework for QoS design
Cluster Computing
KAF: Kalman Filter Based Adaptive Maintenance for Dependability of Composite Services
CAiSE '08 Proceedings of the 20th international conference on Advanced Information Systems Engineering
Non-intrusive performance management for computer services
Proceedings of the ACM/IFIP/USENIX 2006 International Conference on Middleware
An Adaptive Web Services Selection Method Based on the QoS Prediction Mechanism
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
An Adaptive Control System to Deliver Interactive Virtual Environment Content to Handheld Devices
Mobile Networks and Applications
Non-intrusive performance management for computer services
Middleware'06 Proceedings of the 7th ACM/IFIP/USENIX international conference on Middleware
| Hi-index | 0.00 |
Due to the increasing complexity, the behavior of large-scale distributed systems becomes difficult to predict. The ability of on-line identification and autotuning of adaptive control systems has made the adaptive control theoretical design an attractive approach for quality of service (QoS) guarantee. However, there is an inherent constraint in adaptive control systems, i.e. a conflict between asymptotically good control and asymptotically good parameter estimates. This paper addresses these limitations via sensitivity analysis. The simulation study demonstrates that the adaptive control theoretical design depends on the excitation signal, environment uncertainty, and a priori knowledge on the system. In addition, this paper proposes an adaptive dual control framework for mitigating these constraints in QoS design. By incorporating the existing uncertainty of the on-line prediction into the control strategy, the dual adaptive control framework optimizes the tradeoff between the control goal and the uncertainty.