Scalable Utility Aware Scheduling Heuristics for Real-time Tasks with Stochastic Non-preemptive Execution Intervals

Tidwell, Terry; Bass, Carter; Lasker, Eli; Wylde, Micah; Gill, Christopher; Smart, William D.

doi:10.1109/ecrts.2011.30

Cited by 4 publications

(3 citation statements)

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“…(For related problems in this area, cf. Manolache, Eles, and Peng, 2001;Mills and Anderson, 2011;Tidwell, Bass, Lasker, Wylde, Gill, and Smart, 2011.) It should be emphasized that although the computation of the optimal ES policy for 16 jobs takes about 2 hours, the execution of the found ES policy only requires milliseconds of computation time and can therefore also be integrated in a real-time system with very restrictive time limits.…”

Section: Results For Instances With N = 16mentioning

confidence: 98%

Bi-Objective Multi-Mode Project Scheduling Under Risk Aversion

Gutjahr

2015

European Journal of Operational Research

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Section: Results For Instances With N = 16mentioning

confidence: 98%

Bi-Objective Multi-Mode Project Scheduling Under Risk Aversion

Gutjahr

2015

European Journal of Operational Research

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“…RT scheduling is probabilistic, if it applies some laws of probability to select tasks or to compute some timing parameters (Tidwell, 2011). Some probabilistic RT scheduling algorithms model the problem as queues theory.…”

Section: Real Time Schedulingmentioning

confidence: 99%

“…In the context of a PhD thesis, (Tidwell, 2011) defined a Markov Decision Process (MDP) model that enables to derive value-optimal schedulers, and also provides a formal framework for comparing the performance of different scheduling policies. He equally showed how the problem structure allows to bound the number of states in the MDP by wrapping states into a finite number of exemplar states.…”

Section: Reinforcement Learningmentioning

confidence: 99%

AI-Based Methods to Resolve Real-Time Scheduling for Embedded Systems

Boutekkouk

2022

International Journal of Cognitive Informatics and Natural Intelligence

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Artificial Intelligence is becoming more attractive to resolve nontrivial problems including the well known real time scheduling (RTS) problem for Embedded Systems (ES). The latter is considered as a hard multi-objective optimization problem because it must optimize at the same time three key conflictual objectives that are tasks deadlines guarantee, energy consumption reduction and reliability enhancement. In this paper, we firstly present the necessary background to well understand the problematic of RTS in the context of ES, then we present our enriched taxonomies for real time, energy and faults tolerance aware scheduling algorithms for ES. After that, we survey the most pertinent existing works of literature targeting the application of AI methods to resolve the RTS problem for ES notably Constraint Programming, Game theory, Machine learning, Fuzzy logic, Artificial Immune Systems, Cellular Automata, Evolutionary algorithms, Multi-agent Systems and Swarm Intelligence. We end this survey by a discussion putting the light on the main challenges and the future directions.

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