Overload Management in Real-Time Control Applications Using m,k $(m,k)$-Firm Guarantee
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Computers
A Dynamic Priority Assignment Technique for Streams with (m, k)-Firm Deadlines
IEEE Transactions on Computers
Non-Preemptive Scheduling of Real-Time Threads on Multi-Level-Context Architectures
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Graceful Degradation in Real-time Control Applications Using (m, k)-Firm Guarantee
FTCS '97 Proceedings of the 27th International Symposium on Fault-Tolerant Computing (FTCS '97)
Combining (/sub m//sup n/)-hard deadlines and dual priority scheduling
RTSS '97 Proceedings of the 18th IEEE Real-Time Systems Symposium
Power-Aware Scheduling for Periodic Real-Time Tasks
IEEE Transactions on Computers
Energy-efficient dual-voltage soft real-time system with (m,k)-firm deadline guarantee
Proceedings of the 2004 international conference on Compilers, architecture, and synthesis for embedded systems
Optimal Dynamic Voltage Scaling in Energy-Limited Nonpreemptive Systems with Real-Time Constraints
IEEE Transactions on Mobile Computing
Optimal Control of Two-Stage Discrete Event Systems with Real-Time Constraints
Discrete Event Dynamic Systems
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We consider Discrete Event Systems that can dynamically allocate resources in order to process tasks with real-time constraints. In the case of “weakly hard” constraints, a fraction of tasks is allowed to violate them, as long as m out of any k consecutive tasks meet their respective constraints. This is a generalization of a system with purely hard real-time constraints where m = k = 1. For non-preemptive and aperiodic tasks, we formulate an optimization problem where task processing times are controlled so as to minimize a cost function while guaranteeing that a “weakly hard” criterion is satisfied. We establish a number of structural properties of the solution to this problem which lead to an efficient algorithm that does not require any explicit nonlinear programming problem solver. The low complexity of this algorithm makes it suitable for on-line applications. Simulation examples illustrate the performance improvements in such optimally controlled systems compared to ad hoc schemes.