Journal
IEEE TRANSACTIONS ON SERVICES COMPUTING
Volume 8, Issue 4, Pages 563-575Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TSC.2014.2312946
Keywords
Large-scale systems; dimension reduction; LASSO; compressive sensing
Funding
- NSFC [61303013]
- SNSF [12ZR1445700]
- NRF Singapore under CREATE program
- NRF Singapore under key program [313035]
- RFDP of MOE [20120073120039]
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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 L-1-constrained fitting method/Compressive Sensing (CS)-a L-1-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.
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