2,352 research outputs found
QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms
Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017
Practical issues for the implementation of survivability and recovery techniques in optical networks
QoS Considerations in OBS Switched Backbone Net-Works
Optical Burst Switching (OBS) was proposed as a hybrid switching technology solution to handle the multi-Terabit volumes of traffic anticipated to traverse Future Generation backbone Networks. With OBS, incoming data packets are assembled into super-sized packets called data bursts and then assigned an end to end light path. Key challenging areas with regards to OBS Networks implementation are data bursts assembling and scheduling at the network ingress and core nodes respectively as they are key to minimizing subsequent losses due to contention among themselves in the core nodes. These losses are significant contributories to serious degradation in renderable QoS. The paper overviews existing methods of enhancing it at both burst and transport levels. A distributed resources control architecture is proposed together with a proposed wavelength assignment algorithm
Wavelength assignment in all-optical networks for mesh topologies
All-Optical Networks employing Dense Wavelength Division Multiplexing (DWDM) are believed to be the next generation networks that can meet the ever-increasing demand for bandwidth of the end users. This thesis presents some new heuristics for wavelength assignment and converter placement in mesh topologies. Our heuristics try to assign the wavelengths in an efficient manner that results in very low blocking probability. We propose novel static and dynamic assignment schemes that outperform the assignments reported in the literature even when converters are used. The proposed on-line scheme called Round-Robin assignment outperforms previously proposed strategies such as first-fit and random assignment schemes. The performance improvement obtained with the proposed static assignments is very significant when compared with the dynamic schemes. We designed and developed a simulator in the C language that supports the 2D mesh topology with DWDM. We ran extensive simulations and compared our heuristics with those reported in the literature. We have examined converter placement in mesh topologies and proposed that placing converters at the center yields better results than uniform placement when dimension order routing is employed. We introduced a new concept called wavelength assignment with second trial that results in extremely low blocking probabilities when compared to schemes based on a single trial. Our proposed schemes are simple to implement and do not add to the cost. Thus we conclude that wavelength assignment plays more significant role in affecting the blocking probability than wavelength converters. We further conclude that static schemes without converters could easily outperform dynamic schemes thus resulting in great savings
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Statistical assessment of open optical networks
In order to cope with the increase of the final user traffic, operators and vendors are pushing towards physical layer aware networking as a way to maximize the network capacity. To this aim, optical networks are becoming more and more open by exposing physical parameters enabling fast and reliable estimation of the lightpath quality of transmission. This comes in handy not only from the point of view of the planning and managing of the optical paths but also on a more general picture of the whole optical network performance. In this work, the Statistical Network Assessment Process (SNAP) is presented. SNAP is an algorithm allowing for estimating different network metrics such as blocking probability or link saturation, by generating traffic requests on a graph abstraction of the physical layer. Being aware of the physical layer parameters and transceiver technologies enables assessing their impact on high level network figures of merit. Together with a detailed description of the algorithm, we present a comprehensive review of several results on the networking impact of multirate transceivers, flex-grid spectral allocation as a means to finely exploit lightpath capacity and of different Space Division Multiplexing (SDM) solutions
Throughput Maximization in Multi-Band Optical Networks with Column Generation
Multi-band transmission is a promising technical direction for spectrum and
capacity expansion of existing optical networks. Due to the increase in the
number of usable wavelengths in multi-band optical networks, the complexity of
resource allocation problems becomes a major concern. Moreover, the
transmission performance, spectrum width, and cost constraint across optical
bands may be heterogeneous. Assuming a worst-case transmission margin in U, L,
and C-bands, this paper investigates the problem of throughput maximization in
multi-band optical networks, including the optimization of route, wavelength,
and band assignment. We propose a low-complexity decomposition approach based
on Column Generation (CG) to address the scalability issue faced by traditional
methodologies. We numerically compare the results obtained by our CG-based
approach to an integer linear programming model, confirming the near-optimal
network throughput. Our results also demonstrate the scalability of the
CG-based approach when the number of wavelengths increases, with the
computation time in the magnitude order of 10 s for cases varying from 75 to
1200 wavelength channels per link in a 14-node network.Comment: 6 pages, 4 figures, submitted to IEEE International Conference on
Communications 2024 (ICC2024). (Note on arXiv: for beginners in the area of
column generation, please refer to the example computation in the file
. I have uploaded it to this arXiv
project along with other source files.
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