Online adaptive utilization control for real-time embedded multiprocessor systems

Authors:
Jianguo Yao;Xue Liu;Zonghua Gu;Xiaorui Wang;Jian Li
Affiliations:
School of Astronautics, Northwestern Polytechnical University, PR China;Department of Computer Science and Engineering, University of Nebraska - Lincoln, Nebraska, United States;College of Computer Science, Zhejiang University, PR China;Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, United States;School of Software, Shanghai Jiao Tong University, PR China
Venue:
Journal of Systems Architecture: the EUROMICRO Journal
Year:
2010

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Abstract

Many embedded systems have stringent real-time constraints. An effective technique for meeting real-time constraints is to keep the processor utilization on each node at or below the schedulable utilization bound, even though each task's actual execution time may have large uncertainties and deviate a lot from its estimated value. Recently, researchers have proposed solutions based on Model Predictive Control (MPC) for the utilization control problem. Although these approaches can handle a limited range of execution time estimation errors, the system may suffer performance deterioration or even become unstable with large estimation errors. In this paper, we present two online adaptive optimal control techniques, one is based on Recursive Least Squares (RLS) based model identification plus Linear Quadratic (LQ) optimal controller; the other one is based on Adaptive Critic Design (ACD). Simulation experiments demonstrate both the LQ optimal controller and ACD-based controller have better performance than the MPC-based controller and the ACD-based controller has the smallest aggregate tracking errors.