NDLI: Online decentralized adaptive optimal controller design of CPU utilization for Distributed Real-Time Embedded systems

Content Provider	IEEE Xplore Digital Library
Author	Jianguo Yao Xue Liu Xi Chen Xiaorui Wang Jian Li
Copyright Year	2010
Description	Author affiliation: Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada (Jianguo Yao; Xi Chen) \|\| Dept. of Comput. Sci. & Eng., Univ. of Nebraska of Lincoln, Lincoln, NE, USA (Xue Liu) \|\| Sch. of Software, Shanghai Jiao Tong Univ., Shanghai, China (Jian Li) \|\| Dept. of Electr. Eng. & Comput. Sci., Univ. of Tennessee, Knoxville, TN, USA (Xiaorui Wang)
Abstract	In large-scale Distributed Real-time Embedded (DRE) systems, the end-to-end tasks contain chains of subtasks distributed on a large number of CPUs. Controlling their CPU utilizations at desired values is one of the most effective ways to ensure system end-to-end deadlines. For these DRE systems, decentralized control is desired to ensure system scalability and global stability. Recently, researchers have proposed solutions based on Model Predictive Control (MPC) for the decentralized utilization control problem. Although these approaches can handle a limited range of execution time estimation errors, the underlying DRE systems may suffer performance deterioration or even become unstable when large estimation errors exist in real systems. In this paper, we propose a new decentralized optimal controller design for CPU utilization to address this problem. The approach leverages Recursive Least Square (RLS) for adaptive model identification and uses Linear Quadratic (LQ) optimal controller for online tasks' execution rates adjustment. Simulation results demonstrate the proposed approach can ensure good system performance even when large constant or varying execution time estimation errors exist.
Starting Page	283
Ending Page	288
File Size	599374
Page Count	6
File Format	PDF
ISBN	9781424474264
ISSN	07431619
e-ISBN	9781424474271
DOI	10.1109/ACC.2010.5530849
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Programmable control Adaptive control Optimal control Control systems Real time systems Embedded system Estimation error Large-scale systems Distributed control Scalability
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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