Leveraging Deep Learning to Achieve Efficient Resource Allocation with Traffic Evaluation in Datacenter Optical Networks

Yu, Ao; Yang, Hui; He, Linkuan; Xiao, Hongyun; Zhang, Jie

doi:10.1364/ofc.2018.w4i.2

Cited by 26 publications

(16 citation statements)

References 2 publications

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“…The deep-learning-based model outperforms not only conventional resource allocation algorithms but also a single-layer NN-based algorithm in terms of blocking performance and resource occupation efficiency. The results in [91] also bolsters the fact reflected in the previous paragraph about the choice of a ML algorithm. Obviously deep learning, which is more complex than a regular NN learning will be more efficient.…”

Section: A Traffic Prediction and Virtual Topology Designsupporting

confidence: 75%

“…In [91], the authors propose a deep-learning-based traffic prediction and resource allocation algorithm for an intra-datacenter network. The deep-learning-based model outperforms not only conventional resource allocation algorithms but also a single-layer NN-based algorithm in terms of blocking performance and resource occupation efficiency.…”

Section: A Traffic Prediction and Virtual Topology Designmentioning

confidence: 99%

See 1 more Smart Citation

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

486

226

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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.

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Section: A Traffic Prediction and Virtual Topology Designsupporting

confidence: 75%

Section: A Traffic Prediction and Virtual Topology Designmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

486

226

View full text Add to dashboard Cite

show abstract

“…Modulation classification as well as OSNR monitoring was considered in [5], and a deep CNN showed an accurate performance in [6]. Deep learningbased network management and resource allocation were studied in [7] and [8]. Analogously, traffic optimization based on deep reinforcement learning (DRL) was also considered in [9], [10].…”

Section: Modern Deep Learning Applicationsmentioning

confidence: 99%

Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation

Koike‐Akino

Wang

Millar

et al. 2020

J. Lightwave Technol.

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Recently, data-driven approaches motivated by modern deep learning have been applied to optical communications in place of traditional model-based counterparts. The application of deep neural networks (DNN) allows flexible statistical analysis of complicated fiber-optic systems without relying on any specific physical models. Due to the inherent nonlinearity in DNN, various equalizers based on DNN have shown significant potentials to mitigate fiber nonlinearity. In this paper, we propose a turbo equalization (TEQ) based on DNN as a new alternative framework to deal with nonlinear fiber impairments for future coherent optical communications. The proposed DNN-TEQ is constructed with nested deep residual networks (ResNet) to train extrinsic likelihood given soft-information feedback from channel decoding. Through extrinsic information transfer (EXIT) analysis, we verify that our DNN-TEQ can accelerate decoding convergence to achieve a significant gain in achievable throughput by 0.61 b/s/Hz. We also demonstrate that optimizing irregular low-density parity-check (LDPC) codes to match EXIT chart of the DNN-TEQ can improve achievable rates by up to 0.12 b/s/Hz.

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“…A data poisoning scheme is proposed to demonstrate that, the DNN may be contaminate with adversarial samples and degrade the performance of resource reallocation.ML-based traffic prediction methods are also employed in intra-datacenters optical networks. Yu et al present a DNN-based traffic prediction method in datacenter networks, and use the predicted traffic information for resource allocation to improve the resource utilization of high bandwidth and decrease the blocking probability[55]. The predicted traffic properties include traffic arrival time and resource consumption.…”

mentioning

confidence: 99%

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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Leveraging Deep Learning to Achieve Efficient Resource Allocation with Traffic Evaluation in Datacenter Optical Networks

Cited by 26 publications

References 2 publications

An Overview on Application of Machine Learning Techniques in Optical Networks

An Overview on Application of Machine Learning Techniques in Optical Networks

Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation

Machine learning for intelligent optical networks: A comprehensive survey

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