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Control of Large-Scale Systems Through Dimension Reduction

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4 Author(s)
Yao, J. ; J. Yao is with the School of Software, and the Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, Shanghai, 200240 China.( ; Liu, X. ; Zhu, X. ; Guan, H.

Automated physical resource management of large-scale Internet Technology (IT) systems requires 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 new approach for simplified control architecture of large-scale IT systems based on dimension reduction techniques. It combines online selection of critical control knobs through LASSO-a powerful L1-constrained fitting method/Compressive Sensing (CS)-a L1-optimization method, 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 square (RLS) method and a self-tuning linear quadratic (LQ) optimal controller for output regulation. The results of both a numerical simulation in Matlab and a realistic case are presented to demonstrate the effectiveness of our approach.

Published in:

Services Computing, IEEE Transactions on  (Volume:PP ,  Issue: 99 )