Real-Time QoT Estimation Through SDN Control Plane Monitoring Evaluated in Mininet-Optical

Diaz-Montiel, Alan A.; Lantz, Bob; Yu, Jiakai; Kilper, Daniel C.; Ruffini, Marco

doi:10.1109/lpt.2021.3075277

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…It is defined as follows: The number of packets that pass through the switch using the rules stored in the flow table is c. The number of entries sent by the controller to the switch, modifying the flow table so that packets can be handled by the switch, is sc. The service ratio is described by Equation (2).…”

Section: Packet Handling Ratiomentioning

confidence: 99%

“…They show that the SDON concept proves to be more efficient and leads to making the networks flexible and programmable. Similarly, in articles [2][3][4][5], the authors highlight the importance of the SDN concept in controlling the optical part of the backbone network. The authors of [6] show that the OpenFlow protocol has the potential to be extended towards core optical networks.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Flow Timeout Management in Software-Defined Optical Networks

Radamski,

Ząbek,

Domżał

et al. 2024

Photonics

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Current trends in network traffic management rely on the efficient control of individual flows. Software-defined networking popularized this notion. Per-flow management is perfectly viable in standard IP networks, in which packet processing is in the electric domain. However, optical networks provide more restrictions and constraints making per-flow traffic management difficult. One of the most important challenges is to reduce the concurrent number of flows present in the flow tables to make the switching process quicker. In this paper, we propose a mechanism to manage flow timeout values that uses idle timeout and hard timeout parameters. To calculate the appropriate values of the parameters, the mechanism analyzes the packet inter-arrival times. The algorithm also takes into account the current occupancy of the flow table.

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Section: Packet Handling Ratiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Flow Timeout Management in Software-Defined Optical Networks

Radamski,

Ząbek,

Domżał

et al. 2024

Photonics

View full text Add to dashboard Cite

show abstract

“…Many ML technologies to identify and capture such characteristics have been presented in the last decade [21][22][23][24][25][26][27][28][29][30][31][32][33]. Most of these techniques use neural networks (NNs) to approximate the system response.…”

Section: Optical Performance Monitoring Based On MLmentioning

confidence: 99%

Optical path management based on machine learning for optical networks

Shiraki

Mori

Hasegawa

2023

Next-Generation Optical Communication: Components, Sub-Systems, and Systems XII

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The popularity of high-capacity communication services such as video streaming and cloud computing has accelerated the growth in IP traffic. In order to effectively manage and maintain networking systems, various intelligent technologies based on software-defined networking (SDN) have been widely studied. An SDN system that offers flexible optical path management exploiting optical performance monitoring, digital signal processing, and resource allocation is expected to realize higher capacity networks by lowering margins needed to offset system uncertainty. In this paper, we provide a comprehensive survey of optical path management schemes based on machine learning.

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Software for Optical Network Modelling

Adhikari

2022

Optical Switching

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Real-Time QoT Estimation Through SDN Control Plane Monitoring Evaluated in Mininet-Optical

Cited by 9 publications

References 15 publications

Adaptive Flow Timeout Management in Software-Defined Optical Networks

Adaptive Flow Timeout Management in Software-Defined Optical Networks

Optical path management based on machine learning for optical networks

Software for Optical Network Modelling

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