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

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  • Fateh Boutekkouk

    (ReLaCS2 Laboratory, University of Oum el Bouaghi, Algeria)

Abstract

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.

Suggested Citation

  • Fateh Boutekkouk, 2021. "AI-Based Methods to Resolve Real-Time Scheduling for Embedded Systems: A Review," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(4), pages 1-44, October.
    • Handle: RePEc:igg:jcini0:v:15:y:2021:i:4:p:1-44
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