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Adaptive Load Balancing in Learning-Based Approaches for Many-Core Embedded Systems

Fahimeh Farahnakian, Masoumeh Ebrahimi, Masoud Daneshtalab, Liljeberg Pasi, Plosila Juha, Adaptive Load Balancing in Learning-Based Approaches for Many-Core Embedded Systems. Supercomputing 68, 1214–1234, 2014.

Abstract:

Adaptive routing algorithms improve network performance by distributing
traffic over the whole network. However, they require congestion information to facilitate
load balancing. To provide local and global congestion information, we propose
a learning method based on dual reinforcement learning approach. This information
can be dynamically updated according to the changing traffic condition in the network
by propagating data and learning packets. We utilize a congestion detection method
which updates the learning rate according to the congestion level. This method calculates
the average number of free buffer slots in each switch at specific time intervals
and 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 learning
rate is set to a high value, meaning that the global information is more important than
local. In contrast, local is more emphasized than global information in non-congested
switches. Results show that the proposed approach achieves a significant performance
improvement over the traditional Q-routing, DRQ-routing, DBAR and Dynamic XY
algorithms.

BibTeX entry:

@ARTICLE{jFaEbDaPaJu14a,
  title = {Adaptive Load Balancing in Learning-Based Approaches for Many-Core Embedded Systems},
  author = {Farahnakian, Fahimeh and Ebrahimi, Masoumeh and Daneshtalab, Masoud and Pasi, Liljeberg and Juha, Plosila},
  journal = {Supercomputing },
  volume = {68},
  publisher = {Springer},
  pages = {1214–1234},
  year = {2014},
}

Belongs to TUCS Research Unit(s): Embedded Computer and Electronic Systems (ECES)

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