Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
ControlWare: A Middleware Architecture for Feedback Control of Software Performance
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
DEUCON: Decentralized End-to-End Utilization Control for Distributed Real-Time Systems
IEEE Transactions on Parallel and Distributed Systems
Why feedback implementations fail: the importance of systematic testing
Proceedings of the Fifth International Workshop on Feedback Control Implementation and Design in Computing Systems and Networks
Decision making in autonomic computing systems: comparison of approaches and techniques
Proceedings of the 8th ACM international conference on Autonomic computing
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Parcae: a system for flexible parallel execution
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special Section: Extended Version of SASO 2011 Best Paper
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There has been considerable interest in using control theory to build web servers, database managers, and other systems. We claim that the potential value of using control theory cannot be realized in practice without a methodology that addresses controller design, testing, and tuning. Based on our experience with building a controller for the .NET thread pool, we develop a methodology that: (a) designs for extensibility to integrate diverse control techniques, (b) scales the test infrastructure to enable running a large number of test cases, (c) constructs test cases for which the ideal controller performance is known a priori so that the outcomes of test cases can be readily assessed, and (d) tunes controller parameters to achieve good results for multiple performance metrics. We conclude by discussing how our methodology can be extended, especially to designing controllers for distributed systems.