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Article

Adaptive Scheduling for Time-Triggered Network-on-Chip-Based Multi-Core Architecture Using Genetic Algorithm

by *,†,‡, †,‡ and †,‡
Department of Electrical Engineering and Computer Science, University of Siegen, 57076 Siegen, Germany
*
Author to whom correspondence should be addressed.
Current address: Hölderlinstraße 3, 57076 Siegen, Germany.
These authors contributed equally to this work.
Academic Editor: Costas Psychalinos
Electronics 2022, 11(1), 49; https://doi.org/10.3390/electronics11010049
Received: 22 November 2021 / Revised: 19 December 2021 / Accepted: 22 December 2021 / Published: 24 December 2021
(This article belongs to the Special Issue Applications of Embedded Systems, Volume II)
Adaptation in time-triggered systems can be motivated by energy efficiency, fault recovery, and changing environmental conditions. Adaptation in time-triggered systems is achieved by preserving temporal predictability through metascheduling techniques. Nevertheless, utilising existing metascheduling schemes for time-triggered network-on-chip architectures poses design time computation and run-time storage challenges for adaptation using the resulting schedules. In this work, an algorithm for path reconvergence in a multi-schedule graph, enabled by a reconvergence horizon, is presented to manage the state-space explosion problem resulting from an increase in the number of scenarios required for adaptation. A meta-scheduler invokes a genetic algorithm to solve a new scheduling problem for each adaptation scenario, resulting in a multi-schedule graph. Finally, repeated nodes of the multi-schedule graph are merged, and further exploration of paths is terminated. The proposed algorithm is evaluated using various application model sizes and different horizon configurations. Results show up to 56% reduction of schedules necessary for adaptation to 10 context events, with the reconvergence horizon set to 50 time units. Furthermore, 10 jobs with 10 slack events and a horizon of 40 ticks result in a 23% average sleep time for energy savings. Furthermore, the results demonstrate the reduction in the state-space size while showing the trade-off between the size of the reconvergence horizon and the number of nodes of the multi-schedule graph. View Full-Text
Keywords: genetic algorithm; metascheduler; network-on-chip; MPSoC; adaptation; time-triggered systems genetic algorithm; metascheduler; network-on-chip; MPSoC; adaptation; time-triggered systems
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MDPI and ACS Style

Muoka, P.; Onwuchekwa, D.; Obermaisser, R. Adaptive Scheduling for Time-Triggered Network-on-Chip-Based Multi-Core Architecture Using Genetic Algorithm. Electronics 2022, 11, 49. https://doi.org/10.3390/electronics11010049

AMA Style

Muoka P, Onwuchekwa D, Obermaisser R. Adaptive Scheduling for Time-Triggered Network-on-Chip-Based Multi-Core Architecture Using Genetic Algorithm. Electronics. 2022; 11(1):49. https://doi.org/10.3390/electronics11010049

Chicago/Turabian Style

Muoka, Pascal, Daniel Onwuchekwa, and Roman Obermaisser. 2022. "Adaptive Scheduling for Time-Triggered Network-on-Chip-Based Multi-Core Architecture Using Genetic Algorithm" Electronics 11, no. 1: 49. https://doi.org/10.3390/electronics11010049

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