A Multi-Agent Reinforcement Learning Approach to Path Selection in Optical Burst Switching Networks

Kiran, Y. V.; Venkatesh, T. G.; Murthy, C. Siva Ram

doi:10.1109/icc.2009.5198632

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…RL is usually used as optimal actions learning algorithm in decision making agents. approach for path selection in OBS, where all agents, which equipped at ingress nodes, consider actions of other nodes when making their own action [63]. In such scenario, agents will upload their Q-tables to a central server, which will check whether the optimal route of each node shares some common links.…”

Section: Reinforcement Learning-based Routingmentioning

confidence: 99%

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Machine learning for intelligent optical networks: A comprehensive survey

Yang

2020

Journal of Network and Computer Applications

108

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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: Reinforcement Learning-based Routingmentioning

confidence: 99%

“…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]…”

mentioning

confidence: 99%

Machine learning for intelligent optical networks: A comprehensive survey

Yang

2020

Journal of Network and Computer Applications

108

View full text Add to dashboard Cite

show abstract

A Multi-Agent Reinforcement Learning Approach to Path Selection in Optical Burst Switching Networks

Cited by 1 publication

References 12 publications

Machine learning for intelligent optical networks: A comprehensive survey

Machine learning for intelligent optical networks: A comprehensive survey

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