Intelligent storage: Cross-layer optimization for soft real-time workload
ACM Transactions on Storage (TOS)
Elicitation and utilization of application-level utility functions
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Machine learning for on-line hardware reconfiguration
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Autonomous learning of load and traffic patterns to improve cluster utilization
ARCS'07 Proceedings of the 20th international conference on Architecture of computing systems
Engineering autonomic controllers for virtualized web applications
ICWE'10 Proceedings of the 10th international conference on Web engineering
A research agenda for business-driven information technology
HotACI'06 Proceedings of the First international conference on Hot topics in autonomic computing
A survey of formal methods in self-adaptive systems
Proceedings of the Fifth International C* Conference on Computer Science and Software Engineering
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Methods for automatically managing the performance of computing services must estimate a performance model of that service. This paper explores properties that are necessary for performance model estimation of black-box computer systems when used together with adaptive feedback loops. It shows that the standard method of least-squares estimation often gives rise to models that make the control loop perform the opposite action of what is desired. This produces large oscillations and bad tracking performance. The paper evaluates what combination of input and output data provides models with the best properties for the control loop. Plus, it proposes three extensions to the controller that makes it perform well, even when the model estimated would have degraded performance. Our proposed techniques are evaluated with an adaptive controller that provides latency targets for workloads on black-box computer services under a variety of conditions. The techniques are evaluated on two systems: a three-tier ecommerce site and a web server. Experimental results show that our best estimation approach improves the ability of the controller to meet the latency goals significantly. Previously oscillating workload latencies are with our techniques smooth around the latency targets.