Robust Design Against Network Failures of Shared Backup Path Protected SDM-EONs

Abstract

The sustained growth of network traffic has aggravated transmission capacity limitation problems. Recently, elastic optical networks (EONs) based on space division multiplexing (SDM) technology have been proposed to improve spectral efficiency and overcome upper limits on capacity by expanding the spatial dimensions. Accordingly, SDM-EONs can provide higher capacity, higher spectral efficiency, and more flexible optical transmission to address future explosive data growth. However, the critical challenge for the widespread deployment of such complex networks is to ensure their survivability against various types of network failures including core failure, node failure, link failure, and shared risk link group (SRLG) failure. In this paper, we consider the robust design of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -single-mode-fiber-bundle ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -SMFB)-based SDM-EON with shared backup path protection (SBPP). Considering the network reliability against various failures and the uncertainty in traffic volume, we focus on the routing, space, and spectrum assignment (RSSA) problems for the determination of working path and backup path. We formulate the problems as two mixed integer linear programming (MILP) models with the objective of minimizing the maximal frequency slot used (FS) index and the total number of backup FSs. In this scenario, we propose heuristic algorithms for routing decision-making and spectrum assignment. We compare spectral efficiency and execution time among the MILP models, our proposed algorithms, and an existing algorithm. Finally, we employ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Gamma$</tex-math></inline-formula> -robust optimization to handle traffic with uncertainties and compare the simulation results obtained in deterministic and nondeterministic scenarios.