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Providing traffic tolerance in optical packet switching networks: a reinforcement learning approach

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Abstract

The servitization of network resources leads to new challenges for optical networks. For instance, to provide on-demand lightpaths as a service while keeping the probability of packet loss (PPL) low, issues such as lightpath setting up, resource reservation and load balancing must be addressed. We present a self-adaptive framework to process lightpath requests on packet switching optical networks that considers and handles the aforementioned issues. The framework is composed of a dimensioning phase that adds up new resources to an initial topology and a learning phase based on reinforcement learning that provides self-adaptation to tolerate traffic changes. The framework is tested on three realistic mesh topologies achieving a PPL between \(1 \times 10^{-1}\) and \(1 \times 10^{-6}\) for different traffic loads. Compared to fixed multi-path routing strategies, our framework reduces PPL between \(19\%\) and up to \(80\%\). Furthermore, no packet loss can also be achieved for traffic loads equal to or lower than 0.4.

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Authors and Affiliations

  1. Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg

    Iván S. Razo-Zapata

  2. Tecnológico de Monterrey, Monterrey, Mexico

    Gerardo Castañón

  3. Telemática Telemetría Y Radiofrecuencia, Guadalajara, Mexico

    Carlos Mex-Perera

Authors
  1. Iván S. Razo-Zapata
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  2. Gerardo Castañón
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  3. Carlos Mex-Perera
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Correspondence to Iván S. Razo-Zapata.

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Razo-Zapata, I.S., Castañón, G. & Mex-Perera, C. Providing traffic tolerance in optical packet switching networks: a reinforcement learning approach. Photon Netw Commun 34, 307–322 (2017). https://doi.org/10.1007/s11107-017-0699-7

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