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Adaptive Load Balancing in Learning-based Approaches for Many-core Embedded Systems
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2014 (English)In: Journal of Supercomputing, ISSN 0920-8542, E-ISSN 1573-0484, ISSN 0920-8542, Vol. 68, no 3, p. 1214-1234Article in journal (Refereed) Published
Abstract [en]

Adaptive routing algorithms improve network performance by distributingtraffic over the whole network. However, they require congestion information to facilitateload balancing. To provide local and global congestion information, we proposea learning method based on dual reinforcement learning approach. This informationcan be dynamically updated according to the changing traffic condition in the networkby propagating data and learning packets. We utilize a congestion detection methodwhich updates the learning rate according to the congestion level. This method calculatesthe average number of free buffer slots in each switch at specific time intervalsand compares it with maximum and minimum values. Based on the comparison result,the learning rate sets to a value between 0 and 1. If a switch gets congested, the learningrate is set to a high value, meaning that the global information is more important thanlocal. In contrast, local is more emphasized than global information in non-congestedswitches. Results show that the proposed approach achieves a significant performanceimprovement over the traditional Q-routing, DRQ-routing, DBAR and Dynamic XYalgorithms.

Place, publisher, year, edition, pages
2014. Vol. 68, no 3, p. 1214-1234
Keywords [en]
Adaptive routing algorithm, Congestion-aware routing algorithm, Q-learning and Q-routing approaches, Load balancing, Networks-on-Chip, Many-core embedded systems
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Embedded Systems
Identifiers
URN: urn:nbn:se:kth:diva-254866DOI: 10.1007/s11227-014-1166-1OAI: oai:DiVA.org:kth-254866DiVA, id: diva2:1335734
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QC 20190819

Available from: 2019-07-07 Created: 2019-07-07 Last updated: 2019-08-19Bibliographically approved

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Publisher's full texthttp://dx.doi.org/10.1007/s11227-014-1166-1

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Ebrahimi, Masoumeh

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  • apa
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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  • Other locale
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Output format
  • html
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