Quality-of-Control Management in Overloaded Real-Time Systems
IEEE Transactions on Computers
Adaptive Resource Allocation Control for Fair QoS Management
IEEE Transactions on Computers
EURASIP Journal on Embedded Systems - Operating System Support for Embedded Real-Time Applications
Adaptive Fair Sharing Control in Real-Time Systems Using Nonlinear Elastic Task Models
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Journal of Systems Architecture: the EUROMICRO Journal
Schedulability analysis for CAN-based networked control systems with dynamic bandwidth management
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
On self-triggered full-information H-infinity controllers
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
QoC elastic scheduling for real-time control systems
Real-Time Systems
An enhanced dynamic voltage scaling scheme for energy-efficient embedded real-time control systems
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part IV
Third party application control on quality of service in IP based multimedia networks
Information Systems Frontiers
Open Access to Control on Quality of Service in Convergent Networks
International Journal of Information Technology and Web Engineering
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In many application areas, including control systems, careful management of system resources is key to providing the best application performance. Most traditional resource management techniques for real-time systems with multiple control loops are based on open-loop strategies that statically allocate a constant CPU share to each controller, independent of their current resource needs. This provides average control performance with minimal overhead but in general fails to provide the best performance possible within the available resources. We show that by using feedback to dynamically allocate resources to controllers as a function of the current state of their controlled systems, control performance can be significantly improved. We present an optimal resource allocation policy that maximizes control performance within the available resources and provide experimental results showing that the optimal policy 1) significantly increases control performance compared to traditional control system implementations (by more than 20% in our experiments), 2) maximizes control performance over other feedback-based policies, 3) saves resources when perturbations occur infrequently, and 4) incurs negligible overhead.