Routing and Spectrum Allocation in OFDM-Based Optical Networks with Elastic Bandwidth Allocation

• Published in: 2010 IEEE Global Telecommunications Conference GLOBECOM 2010 pp. 1 - 6
• Main Authors: Christodoulopoulos, K, Tomkos, I, Varvarigos, E A
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2010
• Subjects: Algorithm design and analysis; Heuristic algorithms; OFDM; Optical fiber networks; Planning; Resource management; Routing
• ISBN: 1424456363; 9781424456369
• ISSN: 1930-529X
• DOI: 10.1109/GLOCOM.2010.5684008

Orthogonal Frequency Division Multiplexing (OFDM) has been recently proposed as a modulation technique for optical networks, due to its good spectral efficiency and impairment tolerance. Optical OFDM is much more flexible compared to traditional WDM systems, enabling elastic bandwidth transmissions. We consider the planning problem of an OFDM-based optical network where we are given a traffic matrix that includes the requested transmission rates of the connections to be served. Connections are provisioned for their requested rate by elastically allocating spectrum using a variable number of OFDM subcarriers. We introduce the Routing and Spectrum Allocation (RSA) problem, as opposed to the typical Routing and Wavelength Assignment (RWA) problem of traditional WDM networks, and present various algorithms to solve the RSA. We start by presenting an optimal ILP RSA algorithm that minimizes the spectrum used to serve the traffic matrix, and also present a decomposition method that breaks RSA into two substituent subproblems, namely, (i) routing and (ii) spectrum allocation (R+SA) and solves them sequentially. We also propose a heuristic algorithm that serves connections one-by-one and use it to solve the planning problem by sequentially serving all traffic matrix connections. To feed the sequential algorithm, two ordering policies are proposed; a simulated annealing meta-heuristic is also used to obtain even better orderings. Our results indicate that the proposed sequential heuristic with appropriate ordering yields close to optimal solutions in low running times.