Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Feedback–Feedforward Scheduling of Control Tasks
Real-Time Systems
Adaptive Workload Management through Elastic Scheduling
Real-Time Systems
On Quality of Service Optimization with Discrete QoS Options
RTAS '99 Proceedings of the Fifth IEEE Real-Time Technology and Applications Symposium
Elastic Task Model for Adaptive Rate Control
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Convex Optimization
Managing Quality-of-Control Performance Under Overload Conditions
ECRTS '04 Proceedings of the 16th Euromicro Conference on Real-Time Systems
Symbolic quality control for multimedia applications
Real-Time Systems
Online adaptive utilization control for real-time embedded multiprocessor systems
CODES+ISSS '08 Proceedings of the 6th IEEE/ACM/IFIP international conference on Hardware/Software codesign and system synthesis
AQuoSA—adaptive quality of service architecture
Software—Practice & Experience
QoS Control for Pipelines of Tasks Using Multiple Resources
IEEE Transactions on Computers
Low Overhead Dynamic QoS Optimization under Variable Task Execution Times
RTCSA '10 Proceedings of the 2010 IEEE 16th International Conference on Embedded and Real-Time Computing Systems and Applications
Stability Conditions of On-line Resource Managers for Systems with Execution Time Variations
ECRTS '11 Proceedings of the 2011 23rd Euromicro Conference on Real-Time Systems
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Today's embedded systems are exposed to variations in load demand due to complex software applications, dynamic hardware platforms, and the impact of the run-time environment. When these variations are large, and efficiency is required, adaptive on-line resource managers may be deployed on the system to control its resource usage. An often neglected problem is whether these resource managers are stable, meaning that the resource usage is controlled under all possible scenarios. In this paper we develop mathematical models for real-time embedded systems and we derive conditions which, if satisfied, lead to stable systems. For the developed system models, we also determine bounds on the worst case response times of tasks. We also give an intuition of what stability means in a real-time context and we show how it can be applied for several resource managers. We also discuss how our results can be extended in various ways.