Evaluating adaptive resource management for distributed real-time embedded systems
ARM '05 Proceedings of the 4th workshop on Reflective and adaptive middleware systems
A framework for (re)deploying components in distributed real-time and embedded systems
Proceedings of the 2006 ACM symposium on Applied computing
FC-ORB: A robust distributed real-time embedded middleware with end-to-end utilization control
Journal of Systems and Software
Distributed Cooperative Control for Adaptive Performance Management
IEEE Internet Computing
DEUCON: Decentralized End-to-End Utilization Control for Distributed Real-Time Systems
IEEE Transactions on Parallel and Distributed Systems
EURASIP Journal on Embedded Systems - Operating System Support for Embedded Real-Time Applications
FCS/nORB: A feedback control real-time scheduling service for embedded ORB middleware
Microprocessors & Microsystems
Adaptive resource management architecture for distributed real-time embedded systems
Proceedings of the 2009 ACM symposium on Applied Computing
Providing configurable qos management in real-time systems with qos aspect packages
Transactions on Aspect-Oriented Software Development II
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Feedback control real-time scheduling (FCS) aims at satisfying performance specifications of real-time systems based on adaptive resource management. Existing FCS algorithms often rely on the existence of continuous control variables in real-time systems. A number of real-time systems, however, support only a finite set of discrete configurations that limit the adaptation mechanisms. This paper presents Hybrid Supervisory Utilization CONtrol (HySUCON) for scheduling such real-time systems. HySUCON enforces processor utilization bounds by managing the switchings between the discrete configurations. Our approach is based on a best-first-search algorithm that is invoked only if reconfiguration is necessary. Theoretical analysis and simulations demonstrate that the approach leads to robust utilization bounds for varying execution times. Experimental results demonstrate the algorithm performance for a representative application scenario.