Jianguo Yao , 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.(email:firstname.lastname@example.org)
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.
Resource management, Compressed sensing, Quality of service, Control systems, Servers, Vectors, Noise,
Jianguo Yao, Xue Liu, Xiaoyun Zhu, haibing Guan, "Control of Large-Scale Systems Through Dimension Reduction", IEEE Transactions on Services Computing, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TSC.2014.2312946