Real-Time Scheduling Theory and Ada
Computer
Feedback Control Scheduling in Distributed Real-Time Systems
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
RTAS '04 Proceedings of the 10th IEEE Real-Time and Embedded Technology and Applications Symposium
Feedback Utilization Control in Distributed Real-Time Systems with End-to-End Tasks
IEEE Transactions on Parallel and Distributed Systems
Towards a Framework and a Design Methodology for Autonomic SoC
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Distributed Utilization Control for Real-Time Clusters with Load Balancing
RTSS '06 Proceedings of the 27th IEEE International Real-Time Systems Symposium
FC-ORB: A robust distributed real-time embedded middleware with end-to-end utilization control
Journal of Systems and Software
DEUCON: Decentralized End-to-End Utilization Control for Distributed Real-Time Systems
IEEE Transactions on Parallel and Distributed Systems
A control theoretic approach to energy-efficient pipelined computation in MPSoCs
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
Optimal Discrete Rate Adaptation for Distributed Real-Time Systems
RTSS '07 Proceedings of the 28th IEEE International Real-Time Systems Symposium
System level simulation of autonomic SoCs with TAPES
ARCS'08 Proceedings of the 21st international conference on Architecture of computing systems
Online adaptive utilization control for real-time embedded multiprocessor systems
Journal of Systems Architecture: the EUROMICRO Journal
Budget-based control for interactive services with adaptive execution
Proceedings of the 9th international conference on Autonomic computing
A survey and taxonomy of on-chip monitoring of multicore systems-on-chip
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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To provide Quality of Service (QoS) guarantees in open and unpredictable environments, the utilization control problem is defined to keep the processor utilization at the schedulable utilization bound, even in the face of unpredictable and/or varying task execution times. To handle the end-to-end task model where each task is comprised of a chain of subtasks distributed on multiprocessors, researchers have used Model Predictive Control (MPC) to address the Multiple-Input, Multiple-Output (MIMO) control problem. Although MPC can handle a limited range of model uncertainties due to execution time estimation errors, the system may suffer performance deterioration or even become unstable if the actual task execution times are much larger than their estimated values. In this paper, we present an online adaptive optimal control approach using Recursive Least Squares (RLS) based model estimator plus Linear Quadratic (LQ) optimal controller. We use simulation experiments to demonstrate the effectiveness of our controller compared with the MPC-based controller.