Reduced dimension control based on online recursive principal component analysis

Authors:
Jianguo Yao;Xue Liu;Xiaoyun Zhu
Affiliations:
School of Astronautics, Northwestern Polytechnical University, Xi'an, Shaanxi, P.R. China;School of Computer Science, McGill University, Montreal, QC, Canada;VMware, Inc., Palo Alto, CA
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

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Abstract

Automated management of complex information technology applications and systems require dynamic configuration of both application-level and system-level parameters. The existence of large number of tunable parameters makes it difficult to design a feedback controller that adjusts these parameters effectively in order to achieve application-level performance targets. In this paper, we introduce a generic approach for reduced dimension control (RDC) that combines online selection of critical control knobs through online recursive principal component analysis (ORPCA) and adaptive control of the identified knobs. The latter relies on the online estimation of the input-output model with the selected control knobs using the recursive least squares (RLS) method and a self-tuning linear quadratic (LQ) optimal controller for output regulation. The results of a simulation study in Matlab are presented to demonstrate the effectiveness of our RDC approach.