Dynamic Black-Box Performance Model Estimation for Self-Tuning Regulators

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: ...

• Type: Conference
• Year: 2005
• Where: ICAC
• Authors: Magnus Karlsson, Michele Covell
• Keywords: Autonomic Computing, ICAC 2005, Latency Targets, Performance Model, Performance Model Estimation