Loss estimation and control mechanism in bufferless optical packet-switched networks based on multilayer perceptron

Asghari, Masoud; Bagherzadeh, Jamshid; Yousefi, Saleh

doi:10.1007/s11107-017-0743-7

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…To determine the cause of transmission errors at the edge networks, several ML approaches were employed. Congestion may lead to packet loss in optical networks, as shown in [22]. TCP performance may be improved by using classifications.…”

Section: Performance Enhancementsmentioning

confidence: 99%

Application of Machine Learning Technologies for Transport layer Congestion Control

Dong

Yortsos

2022

JCNS

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Due to the advent of technology, humans now live in the modern age of information and data. In this form of world, different objects are interlinked to data sources, and every aspect of human’s lives are recorded in a digital form. For example, the present electronic globe has an abundance of distinct forms of data e.g., health data, social media fata, smartphone data, business data, smart city data, cybersecurity data and Internet of Things (IoT) data, including Covid-19 data. Data can be unstructured, semi-structured and structured, and this is increasing on a daily basis. Machine Learning (ML) is significantly employed in different aspects of real-life e.g., Congestion Control (CC). This paper provides an evaluation of the aspect ML employed in CC. CC has emerged as a fundamental viewpoint in communications system infrastructure in the recent years, since network operations, and network capacity have enhanced at a rapid rate.

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Section: Performance Enhancementsmentioning

confidence: 99%

Application of Machine Learning Technologies for Transport layer Congestion Control

Dong

Yortsos

2022

JCNS

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“…1: An example of message passing on a 6 node topology tificial neural networks (ANN) based on euclidian data, or (ii) geometric deep learning using graph neural networks (GNN), based on graph structured data to perform classification or regression tasks. In 18 an ANN was used to estimate the blocking probability of a network and in 19 , it was used to estimate the fast optical packet loss rate for bufferless optical packetswitched networks. The problem however with ANNs, is that they operate on grid-style data, i.e.…”

Section: Introductionmentioning

confidence: 99%

Intelligent design of optical networks: which topology features help maximise throughput in the nonlinear regime?

Bayvel

Luo

Matzner

et al. 2020

2020 European Conference on Optical Communications (ECOC)

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One of the key performance metrics for optical networks is the maximum achievable throughput for a given network. Determining it however, is an NP-hard optimisation problem, often solved via computationally expensive integer linear programming (ILP) formulations, infeasible to implement as objectives, even on very small node scales of a few tens of nodes. Alternatively heuristics are used, although these too require considerable computation time for large numbers of networks. There is, thus, a need for ultra-fast and accurate performance evaluation of optical networks. For the first time, we propose the use of a geometric deep learning model, message passing neural networks (MPNN), to learn the relationship between, node and edge features, the structure and the maximum achievable throughput of networks. We demonstrate that MPNNs can accurately predict the maximum achievable throughput while reducing the computational time by up to 5-orders of magnitude compared to the ILP for small networks (10-15 nodes) and compared to the heuristic for large networks (25-100 nodes) -proving their suitability for the design and optimisation of optical networks on different time-and distance-scales.

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Machine Learning-Based Channel Analysis for User Concentric Optical Switching Networks

AlZubi

Alarifi

Alnumay

2019

Circuits Syst Signal Process

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Loss estimation and control mechanism in bufferless optical packet-switched networks based on multilayer perceptron

Cited by 4 publications

References 35 publications

Application of Machine Learning Technologies for Transport layer Congestion Control

Application of Machine Learning Technologies for Transport layer Congestion Control

Intelligent design of optical networks: which topology features help maximise throughput in the nonlinear regime?

Machine Learning-Based Channel Analysis for User Concentric Optical Switching Networks

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