Network Fragmentation Measure in Elastic Optical Networks

Bonani, Luiz Henrique; Forghani-elahabad, Majid; Abbade, Marcelo L. F.

doi:10.1109/sbfoton-iopc.2019.8910225

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…1) Average network EFM: This parameters gives the information about maximum available spatial FS block size along a route [30]. It is given in Eq.…”

Section: Stage 3: Programing An Algorithm To Extract/sort Useful Info...mentioning

confidence: 99%

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An Iterative Optimization Approach for Routing, Modulation, and Categorical Spatial Bandwidth Block Allocation to Improve Network Performance for Dynamic Traffic in Elastic Optical Networks

et al. 2022

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“…1) Average network EFM: This parameters gives the information about maximum available spatial FS block size along a route [30]. It is given in Eq.…”

Section: Stage 3: Programing An Algorithm To Extract/sort Useful Info...mentioning

confidence: 99%

“…It is given by a mathematical expression in Eq. ( 11) [12], [30]. The value of average network ABPM ranges from 0 to 1 and shows spatial fragmentation.…”

Section: Efm =mentioning

confidence: 99%

An Iterative Optimization Approach for Routing, Modulation, and Categorical Spatial Bandwidth Block Allocation to Improve Network Performance for Dynamic Traffic in Elastic Optical Networks

et al. 2022

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“…While this metric captures fragmentation due to both continuity and contiguity both, but it is path/connection request specific. In [13], authors used two network-level metrics, one associated with the contiguity aspect and the other associated with network utilization. The first metric uses the link consecutiveness aspect in all links at specific observation periods (time-weighted) to decide on a network's fragmentation level.…”

Section: B Spectrum Fragmentationmentioning

confidence: 99%

A Vectored Fragmentation Metric for Elastic Optical Networks

Sharma¹,

Lohani²,

Singh³

2021

Preprint

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When circuits are set up and dismantled dynamically in elastic optical networks, spectrum tends to become fragmented in the fiber links. The fragmentation limits the available path choices and may lead to significant blocking of connection requests. There are two types of fragmentation in the network spectrum-in the links due to contiguity constraints and over the paths due to continuity. Study of fragmentation and its management is essential to operate the networks efficiently. This paper proposes a vectored fragmentation metric for characterizing the fragmentation, which covers both types of fragmentation. We discuss the characteristics of this metric in both transient and steady-state of the dynamic network. We also test the proposed metric for connection requests' granularity range, arrival rates and holding times, to establish functionality of this metric. We also compare the link-based fragmentation metric with our Vectored Fragmentation Metric to understand the better representation.

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“…In addition, when the bandwidth demands of the connection must be satisfied with more than one FSU, they must be contiguous throughout the frequency spectrum. Because of any solution to the RMLSA problem, unusable FSUs may appear among those assigned, a problem known as spectrum fragmentation [2], [6]- [8]. In transparent optical networks, the spectral fragmentation problem may represent a significant waste of bandwidth because there are no signal conversion capabilities.…”

Section: Introductionmentioning

confidence: 99%

A Bandwidth-Balanced RMLSA Solution for Static Elastic Optical Network: A Two Stages Approach

2022

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This paper addresses one of the main tasks in transparent static elastic optical networks (EONs), known as the routing, modulation level, and spectrum assignment (RMLSA) problem. We present, for the first time, a two-stage RMLSA solution, focusing on reducing spectrum consumption (or network capacity). The first macro stage, called least demand bandwidth balance (LDBB), relies on a physical layer impairment (PLI) model to jointly compute the connections' route and modulation level (RML). We use a new balancing criterion that effectively distributes each network link's frequency slot unit (FSU) demands, exemplifying three different balancing functions based on the maximum number of FSUs on the links and the total number of FSUs demanded and the cost of the route. In the last macro stage, using all connections chosen paths, we perform the spectrum assignment (SA) process using two specific connections prioritization criteria. We propose two SA algorithms, called sliding-fit (SF) and parcel-fit (PF), reducing the spectrum consumption. These algorithms change the SA paradigm by searching connections for a given subset of the frequency spectrum, contrary to the search of FSUs for a given connection in standard approaches. In all cases, our solution exhibited a lower total network capacity than the commonly used strategies found in the literature, with an average network capacity reduction of 5.7 % FSUs. In addition, our proposal may be used to easily dimension network capacity and determine how many extra resources may be needed to attend to all network users.

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Network Fragmentation Measure in Elastic Optical Networks

Cited by 11 publications

References 12 publications

An Iterative Optimization Approach for Routing, Modulation, and Categorical Spatial Bandwidth Block Allocation to Improve Network Performance for Dynamic Traffic in Elastic Optical Networks

An Iterative Optimization Approach for Routing, Modulation, and Categorical Spatial Bandwidth Block Allocation to Improve Network Performance for Dynamic Traffic in Elastic Optical Networks

A Vectored Fragmentation Metric for Elastic Optical Networks

A Bandwidth-Balanced RMLSA Solution for Static Elastic Optical Network: A Two Stages Approach

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