1,012 research outputs found

    Investigation of the tolerance of wavelength-routed optical networks to traffic load variations.

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    This thesis focuses on the performance of circuit-switched wavelength-routed optical network with unpredictable traffic pattern variations. This characteristic of optical networks is termed traffic forecast tolerance. First, the increasing volume and heterogeneous nature of data and voice traffic is discussed. The challenges in designing robust optical networks to handle unpredictable traffic statistics are described. Other work relating to the same research issues are discussed. A general methodology to quantify the traffic forecast tolerance of optical networks is presented. A traffic model is proposed to simulate dynamic, non-uniform loads, and used to test wavelength-routed optical networks considering numerous network topologies. The number of wavelengths required and the effect of the routing and wavelength allocation algorithm are investigated. A new method of quantifying the network tolerance is proposed, based on the calculation of the increase in the standard deviation of the blocking probabilities with increasing traffic load non-uniformity. The performance of different networks are calculated and compared. The relationship between physical features of the network topology and traffic forecast tolerance is investigated. A large number of randomly connected networks with different sizes were assessed. It is shown that the average lightpath length and the number of wavelengths required for full interconnection of the nodes in static operation both exhibit a strong correlation with the network tolerance, regardless of the degree of load non-uniformity. Finally, the impact of wavelength conversion on network tolerance is investigated. Wavelength conversion significantly increases the robustness of optical networks to unpredictable traffic variations. In particular, two sparse wavelength conversion schemes are compared and discussed: distributed wavelength conversion and localized wavelength conversion. It is found that the distributed wavelength conversion scheme outperforms localized wavelength conversion scheme, both with uniform loading and in terms of the network tolerance. The results described in this thesis can be used for the analysis and design of reliable WDM optical networks that are robust to future traffic demand variations

    Performance Evaluation of Non-Hitless Spectrum Defragmentation Algorithms in Elastic Optical Networks

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    Fragmentation in Elastic Optical Networks is an issue caused by isolated, non-aligned, and non-contiguous frequency slots that can not be used to allocate new connection request to the network, due to the optical layer restrictions imposed to the Routing and Spectrum Assignment (RSA) algorithms. To deal with this issue, several studies about Spectrum Defragmentation have been presented. In this work, we analyze the most important Non-Hitless Defragmentation Algorithms found in the literature, with proactive and reactive approaches that include rerouting and non-rerouting schemes, and compare their performance in terms of Blocking Probability, Entropy, and Bandwidth Fragmentation Ratio. Simulations results showed that the Fragmentation Aware schemes outperformed the other algorithms in low traffic load, but the Reactive schemes got better results in high traffic load.Sociedad Argentina de InformĂĄtica e InvestigaciĂłn Operativa (SADIO

    Towards demonstration of photonic payload for telecom satellites

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    To address the challenges of the Digital Agenda for Europe (DAE) and also to remain in line with the evolution of terrestrial communications in a globally connected world, a major increase in telecoms satellites capacity is required in the near future. With telecom satellites payloads based on traditional RF equipment, increase in capacity and flexibility has always translated into a more or less linear increase in equipment count, mass, power consumption and power dissipation. The main challenge of next generation of High Throughput Satellites (HTS) is therefore to provide a ten-fold-increased capacity with enhanced flexibility while maintaining the overall satellite within a "launchable" volume and mass envelope [1], [2], [3]. Photonic is a very promising technology to overcome the above challenges. The ability of Photonic to handle high data rates and high frequencies, as well as enabling reduced size, mass, immunity to EMI and ease of harness routing (by using fibre-optic cables) is critical in this scenario

    Architecture, design, and modeling of the OPSnet asynchronous optical packet switching node

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    An all-optical packet-switched network supporting multiple services represents a long-term goal for network operators and service providers alike. The EPSRC-funded OPSnet project partnership addresses this issue from device through to network architecture perspectives with the key objective of the design, development, and demonstration of a fully operational asynchronous optical packet switch (OPS) suitable for 100 Gb/s dense-wavelength-division multiplexing (DWDM) operation. The OPS is built around a novel buffer and control architecture that has been shown to be highly flexible and to offer the promise of fair and consistent packet delivery at high load conditions with full support for quality of service (QoS) based on differentiated services over generalized multiprotocol label switching

    Performance Evaluation of Non-Hitless Spectrum Defragmentation Algorithms in Elastic Optical Networks

    Get PDF
    Fragmentation in Elastic Optical Networks is an issue caused by isolated, non-aligned, and non-contiguous frequency slots that can not be used to allocate new connection request to the network, due to the optical layer restrictions imposed to the Routing and Spectrum Assignment (RSA) algorithms. To deal with this issue, several studies about Spectrum Defragmentation have been presented. In this work, we analyze the most important Non-Hitless Defragmentation Algorithms found in the literature, with proactive and reactive approaches that include rerouting and non-rerouting schemes, and compare their performance in terms of Blocking Probability, Entropy, and Bandwidth Fragmentation Ratio. Simulations results showed that the Fragmentation Aware schemes outperformed the other algorithms in low traffic load, but the Reactive schemes got better results in high traffic load.Sociedad Argentina de InformĂĄtica e InvestigaciĂłn Operativa (SADIO

    IP multicast over WDM networks

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    Ph.DDOCTOR OF PHILOSOPH

    Telecommunications Networks

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    This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

    Machine learning for optical fiber communication systems: An introduction and overview

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    Optical networks generate a vast amount of diagnostic, control and performance monitoring data. When information is extracted from this data, reconfigurable network elements and reconfigurable transceivers allow the network to adapt both to changes in the physical infrastructure but also changing traffic conditions. Machine learning is emerging as a disruptive technology for extracting useful information from this raw data to enable enhanced planning, monitoring and dynamic control. We provide a survey of the recent literature and highlight numerous promising avenues for machine learning applied to optical networks, including explainable machine learning, digital twins and approaches in which we embed our knowledge into the machine learning such as physics-informed machine learning for the physical layer and graph-based machine learning for the networking layer
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