Providing traffic tolerance in optical packet switching networks: a reinforcement learning approach
Authors
I. S. Razo-Zapata, G. Castañón, and C. Mex-Perera
Reference
Photonic Network Communications, vol. 34, no.3, pp 307–322, 2017
Description
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 1×10−1 and 1×10−6 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.
