Cross Domain QoS Mapping between WMN and Fixed Topology for End-to-End QoS Guarantee

Informatics ◽  
2010 ◽  
Author(s):  
H. Jabeen ◽  
M.u. Rehman ◽  
M. Bano
Keyword(s):  
Qos Guarantee ◽  
Cross Domain ◽  
Fixed Topology ◽  
Qos Mapping ◽  
End To End

End-to-End QoS Guarantee for Delay-Sensitive Mobile Multimedia Conferencing

2014 ◽  
Vol 36 (7) ◽  
pp. 1399-1412
Author(s):  
Ji-Yan WU ◽  
Xiu-Quan QIAO ◽  
Bo CHENG ◽  
Jun-Liang CHEN ◽  
Yun-Lei SUN
Keyword(s):  
Mobile Multimedia ◽  
Qos Guarantee ◽  
Delay Sensitive ◽  
End To End ◽  
Multimedia Conferencing

scholarly journals A Proposal for End-to-End QoS Provisioning in Software-Defined Networks

2017 ◽  
Vol 7 (4) ◽  
pp. 2261 ◽  
Author(s):  
Francesco Lucrezia ◽  
Guido Marchetto ◽  
Fulvio Risso ◽  
Michele Santuari ◽  
Matteo Gerola
Keyword(s):  
Smart Grids ◽  
Service Level ◽  
End User ◽  
Delay Bound ◽  
Qos Guarantee ◽  
End To End Delay ◽  
Network Controller ◽  
Edge Network ◽  
End To End

This paper describes a framework application for the control plane of a network infrastructure; the objective is to feature end-user applications with the capability of requesting at any time a customised end-to-end Quality-of-Service profile in the context of dynamic Service-Level-Agreements. Our solution targets current and future real-time applications that require tight QoS parameters, such as a guaranteed end-to-end delay bound. These applications include, but are not limited to, health-care, mobility, education, manufacturing, smart grids, gaming and much more. We discuss the issues related to the previous Integrated Service and the reason why the RSVP protocol for guaranteed QoS did not take off. Then we present a new signaling and resource reservation framework based on the cutting-edge network controller ONOS.  Moreover, the presented system foresees the need of considering the edges of the network, where terminal applications are connected to, to be piloted by distinct logically centralised controllers. We discuss a possible inter-domain communication mechanism to achieve the end-to-end QoS guarantee.

scholarly journals End-to-End Visual Domain Adaptation Network for Cross-Domain 3D CPS Data Retrieval

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 118630-118638 ◽  
Author(s):  
An-An Liu ◽  
Shu Xiang ◽  
Wei-Zhi Nie ◽  
Dan Song
Keyword(s):  
Domain Adaptation ◽  
Data Retrieval ◽  
Cross Domain ◽  
End To End ◽  
Visual Domain

scholarly journals End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification

2017 ◽  
Author(s):  
Zheng Li ◽  
Yu Zhang ◽  
Ying Wei ◽  
Yuxiang Wu ◽  
Qiang Yang
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Sentiment Classification ◽  
Classification Error ◽  
Target Domain ◽  
Learning Methods ◽  
Cross Domain ◽  
Memory Network ◽  
End To End ◽  
Two Parameter

Domain adaptation tasks such as cross-domain sentiment classification have raised much attention in recent years. Due to the domain discrepancy, a sentiment classifier trained in a source domain may not work well when directly applied to a target domain. Traditional methods need to manually select pivots, which behave in the same way for discriminative learning in both domains. Recently, deep learning methods have been proposed to learn a representation shared by domains. However, they lack the interpretability to directly identify the pivots. To address the problem, we introduce an end-to-end Adversarial Memory Network (AMN) for cross-domain sentiment classification. Unlike existing methods, our approach can automatically capture the pivots using an attention mechanism. Our framework consists of two parameter-shared memory networks: one is for sentiment classification and the other is for domain classification. The two networks are jointly trained so that the selected features minimize the sentiment classification error and at the same time make the domain classifier indiscriminative between the representations from the source or target domains. Moreover, unlike deep learning methods that cannot tell us which words are the pivots, our approach can offer a direct visualization of them. Experiments on the Amazon review dataset demonstrate that our approach can significantly outperform state-of-the-art methods.

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