Maximizing time-dependent spectrum sharing between neighbouring channels in CO-OFDM optical networks

Shen, Gangxiang; Yang, Qi; You, Shanhong; Shao, Weidong

doi:10.1109/icton.2011.5970769

Cited by 24 publications

(12 citation statements)

References 4 publications

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“…Once a connection is established the spectrum width can be dynamically adapted (if feasible) in response to bandwidth variations. The RSA problem for time-varying traffic has been studied in [4]- [7] with the aim of best fitting the bandwidth requirements upon demand variations. A survey regarding the methods developed for the R problem can be found in [8], whereas regarding the SA problem, a number of SA policies have been developed that are in general categorized into fixed, semi-elastic, and elastic [5], [8].…”

Section: Introductionmentioning

confidence: 99%

“…The main difference with the fixed SA policies is that the unutilized slots can now be used for subsequent connection requests providing higher flexibility and better resource utilization. In the elastic SA policies [4]- [7], [9] both the allocated central frequency and the spectrum width can change. The spectrum width can be expanded/reduced according to the actual bandwidth demand and the central frequency can be shifted [5], [6], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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On learning bandwidth allocation models for time-varying traffic in flexible optical networks

Panayiotou¹,

Manousakis²,

Chatzis

et al. 2018

2018 International Conference on Optical Network Design and Modeling (ONDM)

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We examine the problem of bandwidth allocation (BA) on flexible optical networks in the presence of traffic demand uncertainty. We assume that the daily traffic demand is given in the form of distributions describing the traffic demand fluctuations within given time intervals. We wish to find a predictive BA (PBA) model that infers from these distributions the bandwidth that best fits the future traffic demand fluctuations. The problem is formulated as a Partially Observable Markov Decision Process and is solved by means of Dynamic Programming. The PBA model is compared to a number of benchmark BA models that naturally arise after the assumption of traffic demand uncertainty. For comparing all the BA models developed, a conventional routing and spectrum allocation heuristic is used adhering each time to the BA model followed. We show that for a network operating at its capacity crunch, the PBA model significantly outperforms the rest on the number of blocked connections and unserved bandwidth. Most importantly, the PBA model can be autonomously adapted upon significant traffic demand variations by continuously training the model as realtime traffic information arrives into the network.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On learning bandwidth allocation models for time-varying traffic in flexible optical networks

Panayiotou¹,

Manousakis²,

Chatzis

et al. 2018

2018 International Conference on Optical Network Design and Modeling (ONDM)

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“…For an introduction into this topic, we refer to the works of Talebi et al and Wan et al . We remark that the topic is ongoing, with a broad range of publications , focusing on algorithmic and technological solutions of the SA to current and future available technologies. In this context, we point the reader to the work of Paolucci et al , where the authors discuss state of the art validations of elastic SA in flexgrids, in particular based on active Stateful Path Computation Elements (PCE), to handle frequency defragmentation issues.…”

Section: Introductionmentioning

confidence: 99%

Robust spectrum allocation in elastic flexgrid optical networks: Complexity and formulations

et al. 2017

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Flexgrid optical networking technology allows for a more flexible consumption of bandwidth. The spectrum allocation problem consists of the conflict‐free assignment of consecutive spectrum space of different sizes to lightpaths. In this article, we study the computational complexity of spectrum allocation with and without demand uncertainty. First, it is shown that the problem becomes already NP‐hard for cases where wavelength assignment is still polynomial time solvable. Next, five different ways to define the robust counterpart are compared. It is shown (amongst others) that on a single network edge, the two least efficient models are less computationally demanding than the other variants. A computational study using comparable integer linear programming formulations reveals that the additional slots required by these models directly depend on the restrictions of the employed technology. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(4), 342–359 2017

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“…On the contrary, during the hours of low user activity, when the bandwidth requirements are lower, the unused resources are released and assigned to other network services. In this context, adaptive SA with a known a priori 24hour traffic pattern and online adaptation of OFDM subcarriers have been addressed in [22] and [23], respectively.…”

Section: Introductionmentioning

confidence: 99%

Elastic Spectrum Allocation for Time-Varying Traffic in FlexGrid Optical Networks

Klinkowski

Ruiz

Velasco

et al. 2013

IEEE J. Select. Areas Commun.

139

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Elastic flexgrid optical networks (FG-ON) are considered a very promising solution for next-generation optical networks. In this article we focus on lightpath adaptation under variable traffic demands in FG-ON. Specifically, we explore the elastic spectrum allocation (SA) capability of FG-ON and, in this context, we study the effectiveness of three alternative SA schemes in terms of the network performance. To this end, we formulate a Multi-Hour Routing and Spectrum Allocation (MH-RSA) optimization problem and solve it by means of both Integer Linear Programming (ILP) and efficient heuristic algorithms. Since, as numerical results show, the effectiveness of SA schemes highly depends on the traffic demand profile, we formulate some indications on the applicability of elastic SA in FG-ON.

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Maximizing time-dependent spectrum sharing between neighbouring channels in CO-OFDM optical networks

Cited by 24 publications

References 4 publications

On learning bandwidth allocation models for time-varying traffic in flexible optical networks

On learning bandwidth allocation models for time-varying traffic in flexible optical networks

Robust spectrum allocation in elastic flexgrid optical networks: Complexity and formulations

Elastic Spectrum Allocation for Time-Varying Traffic in FlexGrid Optical Networks

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