Enhancing Energy Efficiency of the Doze Mode Mechanism in Ethernet Passive Optical Networks Using Support Vector Regression

Yang, Chengtao; Lotfolahi, Mohammad Amin; Hwang, I‐Shyan; Ab-Rahman, Mohammad Syuhaimi; Nikoukar, AliAkbar; Liem, Andrew Tanny; Ganesan, Elaiyasuriyan

doi:10.3390/photonics9030180

Cited by 3 publications

(2 citation statements)

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“…Similarly, to reduce energy consumption of a transmitter, Yang et al [300] have proposed that an ONU should transit between active state and doze state to conserve energy. The ONU stays in doze state for a longer duration when the uplink traffic has flexible QoS requirement.…”

Section: A Tdm-ponmentioning

confidence: 99%

“…When traffic arrival increases rapidly, to avoid buffer overflow at an ONU, the authors have proposed to interrupt doze state and request for uplink bandwidth to the OLT in the following cycle(s). Although the solutions proposed in [299], [300] are able to show satisfactory QoS performance, their energy saving performance is undermined because of keeping the receiver of an ONU always switched on.…”

Section: A Tdm-ponmentioning

confidence: 99%

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Energy Conservation in Passive Optical Networks: A Tutorial and Survey

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Section: A Tdm-ponmentioning

confidence: 99%

Section: A Tdm-ponmentioning

confidence: 99%

Energy Conservation in Passive Optical Networks: A Tutorial and Survey

Newaz,

Ahvar,

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et al. 2024

IEEE Commun. Surv. Tutorials

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Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

Swamidoss,

Samiayya,

Gunasekar

2024

Concurrency and Computation

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SummaryIn green optical networking, designing an adaptive energy‐saving scheme plays a vital role, in optimizing energy consumption by dynamically adjusting resources based on network traffic and environmental conditions, to a more sustainable and efficient optical communication infrastructure. Traditional methods in optical networking face challenges such as static resource allocation, limited adaptability, inefficient power usage, environmental insensitivity, and scalability issues. Therefore this article proposed a novel method named Dynamic Quality of Service based Random update Genghis Khan (DQ‐RGK) algorithm, the proposed model can tackle the abovementioned complexities. In this study, cluster head dynamic placement is utilized to optimize the network's performance by adapting the placement of cluster heads to the current topology, load distribution, and energy levels in the network nodes. Additionally, Dynamic Quality of Service (QoS) is employed to respond dynamically to changes in network conditions, adapting to varying traffic patterns and resource availability. In this work, the Genghis Khan Shark optimization with a random update strategy is implemented for hyperparameter optimization to enhance the performance of the DQ‐RGK method. The DQ‐RGK adjusts the parameters of QoS in real‐time, and this ensures that network resources based on the requirements changed and priorities of applications, which ultimately optimizes performance and enhances user experience. By dynamically assigning and reallocating resources based on the current demand the algorithm enhances overall network efficiency and reduces energy consumption. Then, this work analyzes the experimental results, where some evaluation measures estimate the DQ‐RGK method's performance. Routing efficiency, latency, scalability, spectral efficiency, Packet Delivery Ratio, throughput, network lifetime, energy consumption, jitter, and energy consumption are the measures employed by the DQ‐RGK model. In The results, other routing models that do not provide efficiency are utilized, a comparison of these other routing models is represented in results. The overall DQ‐RGK model's effectiveness is represented in the experimental results and its effectiveness is greater among other methods.

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LSTM-Based DWBA Prediction for Tactile Applications in Optical Access Network

et al. 2022

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Historically, the optical access network (OAN) plays a crucial role of supporting emerging new services such as 4 k, 8 k multimedia streaming, telesurgery, augmented reality (AR), and virtual reality (VR) applications in the context of Tactile Internet (TI). In order to prevent losing connectivity to the current mobile network and Tactile Internet, the OAN must expand capacity and improve the quality of Services (QoS) mainly for the low latency of 1 ms. The optical network has adopted artificial intelligence (AI) technology, such as deep learning (DL), in order to classify and predict complex data. This trend mainly focuses on bandwidth prediction. The software-defined network (SDN) and cloud technologies provide all the essential capabilities for deploying deep learning to enhance the performance of next-generation ethernet passive optical networks (NG-EPONs). Therefore, in this paper, we propose a deep learning long-short-term-memory model-based predictive dynamic wavelength bandwidth allocation (DWBA) mechanism, termed LSTM-DWBA in NG-EPON. Future bandwidth for the end-user is predicted based on NG-EPON MPCP control messages exchanged between the OLT and ONUs and cycle times. This proposed LSTM-DWBA addresses the uplink control message overhead and QoS bottleneck of such networks. Finally, the extensive simulation results show the packet delay, jitter, packet drop, and utilization.

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Enhancing Energy Efficiency of the Doze Mode Mechanism in Ethernet Passive Optical Networks Using Support Vector Regression

Cited by 3 publications

References 25 publications

Energy Conservation in Passive Optical Networks: A Tutorial and Survey

Energy Conservation in Passive Optical Networks: A Tutorial and Survey

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

LSTM-Based DWBA Prediction for Tactile Applications in Optical Access Network

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