Timeslot-Compactness-aware Routing Wavelength and Timeslot Assignment Strategy for Distributed AI Training in a Multi-granularity All-Optical Metro Spine-Leaf Network

To accommodate the traffic flow from distributed AI training, we propose a timeslot-compactness-aware routing, wavelength and timeslot assignment strategy in a multi-granularity all-optical metro spine-leaf network, which achieves 6.58% less blocking, 14.7% more wavelength efficiency.

Authors

Shaoxiong Feng
Bojun Zhang (ORCID: https://orcid.org/0000-0003-2124-5931)
Yuefeng Ji (ORCID: https://orcid.org/0000-0002-6618-272X)
Jiawei Zhang (ORCID: https://orcid.org/0000-0003-1377-7938)
Zhiqun Gu (ORCID: https://orcid.org/0000-0003-4293-9107)

Institutions

Beijing University of Posts and Telecommunications (CN)

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.1364/ofc.2025.w1a.3
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

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article

Timeslot-Compactness-aware Routing Wavelength and Timeslot Assignment Strategy for Distributed AI Training in a Multi-granularity All-Optical Metro Spine-Leaf Network

Shaoxiong Feng, Bojun Zhang, Yuefeng Ji, Jiawei Zhang et al.

Jan 1, 2025

Neural Networks and Reservoir Computing

article

Timeslot-Compactness-aware Routing Wavelength and Timeslot Assignment Strategy for Distributed AI Training in a Multi-granularity All-Optical Metro Spine-Leaf Network

Shaoxiong Feng, Bojun Zhang, Yuefeng Ji, Jiawei Zhang, Zhiqun Gu

January 1, 2025article en

Abstract

To accommodate the traffic flow from distributed AI training, we propose a timeslot-compactness-aware routing, wavelength and timeslot assignment strategy in a multi-granularity all-optical metro spine-leaf network, which achieves 6.58% less blocking, 14.7% more wavelength efficiency.

Beijing University of Posts and Telecommunications (CN)

Openalex Percentile: Top 94%

Neural Networks and Reservoir Computing

DOI Source OpenAlex

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