Energy-efficient packet transmission over a wireless link
IEEE/ACM Transactions on Networking (TON)
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
Power-Aware Scheduling for Periodic Real-Time Tasks
IEEE Transactions on Computers
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
Optimal Admission Control of Discrete Event Systems with Real-Time Constraints
Discrete Event Dynamic Systems
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We consider Discrete Event Systems (DES) involving tasks with real-time constraints and seek to control processing times so as to minimize a cost function subject to each task meeting its own constraint. It has been shown that the off-line version of this problem can be efficiently solved by the Critical Task Decomposition Algorithm (CTDA) (Mao et al., IEEE Trans Mobile Comput 6(6):678---688, 2007). In the on-line version, random task characteristics (e.g., arrival times) are not known in advance. To bypass this difficulty, worst-case analysis may be used. This, however, does not make use of probability distributions and results in an overly conservative solution. In this paper, we develop a new approach which does not rely on worst-case analysis but provides a "best solution in probability" efficiently obtained by estimating the probability distribution of sample-path-optimal solutions. We introduce a condition termed "non-singularity" under which the best solution in probability leads to the on-line optimal control. Numerical examples are included to illustrate our results and show substantial performance improvements over worst-case analysis.