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Correlation between traffic granularity and defragmentation periodicity in elastic optical networks
Author(s) -
Khodashenas Pouria Sayyad,
Comellas Jaume,
Perelló Jordi,
Spadaro Salvatore
Publication year - 2015
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.2795
Subject(s) - granularity , computer science , transmission (telecommunications) , limiting , optical networking , distributed computing , computer network , jitter , spectrum (functional analysis) , algorithm , real time computing , wavelength division multiplexing , wavelength , telecommunications , optics , physics , engineering , mechanical engineering , operating system , quantum mechanics
While elastic optical network technologies have emerged as promising solutions for future ultra‐high‐speed optical transmission, the unavoidable spectral fragmentation problem that appears in such networks significantly degrades their performance. In light of this, spectral defragmentation technologies have been introduced in elastic optical networks, aiming to increase the spectrum utilisation. During the defragmentation operation, the available fragmented spectrum bands are consolidated by reallocating existing connections, either re‐routing them along alternative routes and/or re‐tuning them onto different spectrum portions. Obviously, spectral defragmentation increases network complexity and cost. Therefore, it is highly desirable to limit its application as much as possible, while keeping network performance within acceptable margins. In this paper, we focus on analyzing the correlation between the optimal (i.e. minimum) spectrum defragmentation periodicity in the network with the granularity of the supported traffic. For this purpose, we initially introduce a novel algorithm for efficient spectrum defragmentation. The proposed algorithm aims to consolidate the available fibre spectrum as much as possible, while limiting the number of reallocated active connections. Then, supported on extensive simulation results, we show how spectral defragmentation periodicity can be effectively configured by having knowledge of the offered traffic granularity. Copyright © 2014 John Wiley & Sons, Ltd.