Graphical Probabilistic Routing Model for OBS Networks with Realistic Traffic Scenario

Lévesque, Martin; Elbiaze, Halima

doi:10.1109/glocom.2009.5425570

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…In [59], Graphic Probabilistic Routing Model (GPRM) which is based on Bayesian Network (BN) is presented to select less utilized links in an Optical Burst Switching (OBS) network to reduce Burst Loss Ratio (BLR) without affecting the end-to-end delay. A BN model is exploited at each node in the network, which determines the next hop according to a routing table updated by the BN.…”

Section: Supervised Learning-based Routingmentioning

confidence: 99%

“…Heuristic-based route planning will suffer high computational complexity when facing large scale topology.To overcome the drawbacks of simple SPF routing and heuristic-based routing, ML techniques have been employed. Some works model the routing allocation as classification and regression tasks, which use supervised learning to obtain the rules of routes generation from the historical route dataset[59] [60][61]. Other works model the routing problem as decision-making tasks, in which the RL is employed to generate optimal routing assignment [62][63]…”

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confidence: 99%

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Machine learning for intelligent optical networks: A comprehensive survey

Yang

2020

Journal of Network and Computer Applications

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With the rapid development of Internet and communication systems, both in services and technologies, communication networks have been suffering increasing complexity. It is imperative to improve intelligence in communication network, and several aspects have been incorporating with Artificial Intelligence (AI) and Machine Learning (ML).Optical network, which plays an important role both in core and access network in communication networks, also faces great challenges of system complexity and the requirement of manual operations. To overcome the current limitations and address the issues of future optical networks, it is essential to deploy more intelligence capability to enable autonomous and flexible network operations. ML techniques are proved to have superiority on solving complex problems; and thus recently, ML techniques have been used for many optical network applications.In this paper, a detailed survey of existing applications of ML for intelligent optical networks is presented. The applications of ML are classified in terms of their use cases, which are categorized into optical network control and resource management, and optical networks monitoring and survivability. The use cases are analyzed and compared according to the used ML techniques. Besides, a tutorial for ML applications is provided from the aspects of the introduction of common ML algorithms, paradigms of ML, and motivations of applying ML. Lastly, challenges and possible solutions of ML application in optical networks are also discussed, which intends to inspire future innovations in leveraging ML to build intelligent optical networks.

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Section: Supervised Learning-based Routingmentioning

confidence: 99%

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confidence: 99%

Machine learning for intelligent optical networks: A comprehensive survey

Yang

2020

Journal of Network and Computer Applications

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“…Very few studies exist on the application of machine learning to classify OBS nodes. In [9,10], authors studied the application of ML in OBS networks. In [6], authors classified the nature of data burst loss in the OBS network into two categories, i.e., loss due to contention and loss due to congestion.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of Semi-Supervised Learning in Detecting Burst Header Packet Flooding Attack in Optical Burst Switching Network

2021

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This paper presents a comparative analysis of four semi-supervised machine learning (SSML) algorithms for detecting malicious nodes in an optical burst switching (OBS) network. The SSML approaches include a modified version of K-means clustering, a Gaussian mixture model (GMM), a classical self-training (ST) model, and a modified version of self-training (MST) model. All the four approaches work in semi-supervised fashion, while the MST uses an ensemble of classifiers for the final decision making. SSML approaches are particularly useful when a limited number of labeled data is available for training and validation of the classification model. Manual labeling of a large dataset is complex and time consuming. It is even worse for the OBS network data. SSML can be used to leverage the unlabeled data for making a better prediction than using a smaller set of labelled data. We evaluated the performance of four SSML approaches for two (Behaving, Not-behaving), three (Behaving, Not-behaving, and Potentially Not-behaving), and four (No-Block, Block, NB- wait and NB-No-Block) class classifications using precision, recall, and F1 score. In case of the two-class classification, the K-means and GMM-based approaches performed better than the others. In case of the three-class classification, the K-means and the classical ST approaches performed better than the others. In case of the four-class classification, the MST showed the best performance. Finally, the SSML approaches were compared with two supervised learning (SL) based approaches. The comparison results showed that the SSML based approaches outperform when a smaller sized labeled data is available to train the classification models.

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“…This routing table is used when the BHP attempts (in the electronic domain) to reserve the ressource for the data burst. GPRM is composed of four evidences and one decision node for each possible next hop [4]. A lookup to the routing table is done according to evidences in order to successively get the best next hop in terms of probability of success to reach the destination.…”

Section: Graphical Probabilistic Routing Model For Obs Networkmentioning

confidence: 99%

Cognitive mechanisms for providing QoS in OBS networks

Elbiaze

2011

2011 13th International Conference on Transparent Optical Networks

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The main weakness of OBS (Optical Burst Switching) networks is the difficulty to support different types of traffic requiring several performance constraints. This paper focuses on the design and the performance evaluation of efficient cognitive OBS protocols that pave a way to the multi-service context. We present a graphical probabilistic model to avoid and resolve contention inside the OBS network. In order to efficiently exploit the available bandwidth while respecting QoS requirements, we highlight our complete framework using closed/opoen loop feedback control theoretic techniques to improve OBS networks perfomance.

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Graphical Probabilistic Routing Model for OBS Networks with Realistic Traffic Scenario

Cited by 10 publications

References 8 publications

Machine learning for intelligent optical networks: A comprehensive survey

Machine learning for intelligent optical networks: A comprehensive survey

A Comparative Analysis of Semi-Supervised Learning in Detecting Burst Header Packet Flooding Attack in Optical Burst Switching Network

Cognitive mechanisms for providing QoS in OBS networks

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