2018
DOI: 10.1049/iet-net.2017.0101
|View full text |Cite
|
Sign up to set email alerts
|

Comparative study of congestion notification techniques for hop‐by‐hop‐based flow control in data centre Ethernet

Abstract: Data centre Ethernet (DCE) is a budding research area that has received considerable attention from the ICT sector. The traditional DCEs are considered unreliable despite being widely used in modern day data centres. In Ethernet intermediate layer 2 switching devices, the outgoing traffic is faster than the incoming traffic and therefore results in packet drops. Ethernet reliability is provided by the upper layer protocols, which is outlaw to the initial concept of the network. As such, various congestion noti… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
4

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 54 publications
(108 reference statements)
0
2
0
Order By: Relevance
“…An improved lesion image extraction method is proposed in [44], which uses a combination of multi-scale morphological local variance reconstruction and fast fuzzy C-means clustering. Improved skin cancer prediction methods are developed by researchers such as an all-inclusive application [45], combination of adaptive region growth and neuromorphic clustering [46], hybrid metaheuristics for enhance image boundaries estimation [47], DL-based technique BF2SkNet for optimal feature [48], deep neural network with features fusion and selection [49], hybrid deep whale optimization with entropy-mutual information (EMI) method [50], enhanced classification technique [51], modified meta-heuristic technique for feature selection [52], a hybrid classification method with feature optimization [53], enhanced cost estimation using adaptive multi-cost function [54,55], and an optimal feature extraction using the Henry Gas Solubility Optimization algorithm [56].…”
Section: Dataset Attributesmentioning
confidence: 99%
“…An improved lesion image extraction method is proposed in [44], which uses a combination of multi-scale morphological local variance reconstruction and fast fuzzy C-means clustering. Improved skin cancer prediction methods are developed by researchers such as an all-inclusive application [45], combination of adaptive region growth and neuromorphic clustering [46], hybrid metaheuristics for enhance image boundaries estimation [47], DL-based technique BF2SkNet for optimal feature [48], deep neural network with features fusion and selection [49], hybrid deep whale optimization with entropy-mutual information (EMI) method [50], enhanced classification technique [51], modified meta-heuristic technique for feature selection [52], a hybrid classification method with feature optimization [53], enhanced cost estimation using adaptive multi-cost function [54,55], and an optimal feature extraction using the Henry Gas Solubility Optimization algorithm [56].…”
Section: Dataset Attributesmentioning
confidence: 99%
“…Figure 1: Architecture of WBAN Despite many benefits, WBANs suffer from several limitations and challenges due to miniaturized nodes with shorter battery lives. Smart health application using WBAN requires seamless network connectivity and autonomous self-organizing capabilities of inter-connected sensor nodes [6]. Due to resource limitations in terms of battery power, available bandwidth, transmission power, buffer size, and processing capacity, the traditional routing protocols designed for Wireless Sensor Networks (WSNs) are not appropriate for WBANs [7].…”
Section: Introductionmentioning
confidence: 99%