Flexgrid technology is now considered to be a promising solution for future high-speed network design. In this context, we need a tutorial that covers the key aspects of elastic optical networks. This tutorial paper starts with a brief introduction of the elastic optical network and its unique characteristics. The paper then moves to the architecture of the elastic optical network and its operation principle. To complete the discussion of network architecture, this paper focuses on the different node architectures, and compares their performance in terms of scalability and flexibility. Thereafter, this paper reviews and classifies routing and spectrum allocation (RSA) approaches including their pros and cons. Furthermore, various aspects, namely, fragmentation, modulation, quality-of-transmission, traffic grooming, survivability, energy saving, and networking cost related to RSA, are presented. Finally, the paper explores the experimental demonstrations that have tested the functionality of the elastic optical network, and follows that with the research challenges and open issues posed by flexible networks.

Routing and Spectrum Allocation in Elastic Optical Networks: A Tutorial

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey

Study and Analysis of Routing and Spectrum Allocation (RSA) and Routing, Modulation and Spectrum Allocation (RMSA) Algorithms in Elastic Optical Networks (EONs)

High spectrum efficiency and fast restoration speed are highly desired for survivable elastic optical networks (EONs). In this paper, we take the advantages of failure-independent path-protecting preconfigured cycles (FIPP p -cycles) and investigate how to realize spectrum efficient resilience design with them. We first study the problem of offline service provisioning with FIPP p -cycles. We formulate an integer linear programming model and prove that the problem is $\mathcal{NP}$ hard. Then, several time-efficient heuristics are designed for FIPP p -cycle formulation and related routing, modulation format, and spectrum assignment. Extensive simulations on offline provisioning verify that the heuristics can obtain near-optimal solutions. Next, we consider online service provisioning with FIPP p -cycles in dynamic EONs. In order to overcome the decrease of protection efficiency during dynamic network operation, we propose a p -cycle reconfiguration scheme to reoptimize protection structures on-the-fly. Simulation results demonstrate that the proposed algorithms can improve spectrum efficiency and reduce bandwidth blocking probability simultaneously.

On Spectrum Efficient Failure-Independent Path Protection p -Cycle Design in Elastic Optical Networks

Contemporary society relies on fast and reliable transfer of information, leading to ever-increasing size of transmitted data that stimulates the development of new algorithms for network management and optimization. This paper covers problem of allocating three types of network flows (unicast, anycast, multicast) in elastic optical network that implements dedicated path protection. The problem answers some users expectations such as efficient realization of cloud- and content-related services (by means of three flow types) and provision of network survivability. The problem is modelled as an integer linear programming with respect to two optimization criteria related to the spectrum usage. Next, solution algorithms are proposed which include, among others, two metaheuristics based on particle swarm optimization and tabu search. The results of simulations evaluate efficiency of the algorithms and prove that the proposed tabu search approach significantly outperforms other methods.

Two metaheuristics for routing and spectrum allocation in cloud-ready survivable elastic optical networks

The paper studies efficient modeling and prediction of daily traffic patterns in transport telecommunication networks. The investigation is carried out using two historical datasets, namely WASK and SIX, which collect flows from edge nodes of two networks of different size. WASK is a novel dataset introduced and analyzed for the first time in this paper, while SIX is a well-known source of network flows. For the considered datasets, the paper proposes traffic modeling and prediction methods. For traffic modeling, the Fourier Transform is applied. For traffic prediction, two approaches are proposed—modeling-based (the forecasting model is generated based on historical traffic models) and machine learning-based (network traffic is handled as a data stream where chunk-based regression methods are applied for forecasting). Then, extensive simulations are performed to verify efficiency of the approaches and their comparison. The proposed modeling method revealed high efficiency especially for the SIX dataset, where the average error was lower than 0.1%. The efficiency of two forecasting approaches differs with datasets–modeling-based methods achieved lower errors for SIX while machine learning-based for WASK. The average prediction error for SIX reached 3.36% while forecasting for WASK turned out extremely challenging.

Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study

On the efficient optimization of unicast, anycast and multicast flows in survivable elastic optical networks

Artificial Bee Colony for optimization of cloud-ready and survivable elastic optical networks

Modeling and optimization of data center location and routing and spectrum allocation in survivable elastic optical networks

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