Machine Learning Empowered Intelligent Data Center Networking

Wang, Ting; Li, Bo; Chen, Mingsong; Yu, Shui

doi:10.1007/978-981-19-7395-6_3

Cited by 2 publications

(3 citation statements)

References 170 publications

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“…channel, for obtaining the without idle channels, moving to another search is the most important. Below Equation (6) shows the input of the routing model:…”

Section: Device-to-device Communicationmentioning

confidence: 99%

“…The proposed solutions and future directions are important for the purpose of resolving the challenges of the SMAC 5 . Extensive research and practical applications of machine learning (ML) in data center networks have resulted in numerous accomplishments 6 . By vigilant sleep mode (WSM), the optical network terminal's (ONT) optical receiver (RX) is activated and deactivated in the cyclic method.…”

Section: Introductionmentioning

confidence: 99%

“…5 Extensive research and practical applications of machine learning (ML) in data center networks have resulted in numerous accomplishments. 6 By vigilant sleep mode (WSM), the optical network terminal's (ONT) optical receiver (RX) is activated and deactivated in the cyclic method. This WSM is the combination of the cyclic doze mode (CDM) and cyclic sleep mode (CSM).…”

Section: Introductionmentioning

confidence: 99%

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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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“…channel, for obtaining the without idle channels, moving to another search is the most important. Below Equation (6) shows the input of the routing model:…”

Section: Device-to-device Communicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%