A Machine Learning Solution for Automatic Network Selection to Enhance Quality of Service for Video Delivery

2021 ◽  
Author(s):  
Devanshu Anand ◽  
Mohammed Amine Togou ◽  
Gabriel-Miro Muntean
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Network Selection ◽  
Video Delivery

scholarly journals Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias throughout the Machine Learning Lifecyle

2021 ◽  
pp. 1-47
Author(s):  
Yang Trista Cao ◽  
Hal Daumé
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
English Text ◽  
Coreference Resolution ◽  
Resolution System ◽  
Building Systems ◽  
Systematic Biases ◽  
Build Systems

Abstract Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such inferences raise the risk of systematic biases in coreference resolution systems, including biases that can harm binary and non-binary trans and cis stakeholders. To better understand such biases, we foreground nuanced conceptualizations of gender from sociology and sociolinguistics, and investigate where in the machine learning pipeline such biases can enter a coreference resolution system. We inspect many existing datasets for trans-exclusionary biases, and develop two new datasets for interrogating bias in both crowd annotations and in existing coreference resolution systems. Through these studies, conducted on English text, we confirm that without acknowledging and building systems that recognize the complexity of gender, we will build systems that fail for: quality of service, stereotyping, and over- or under-representation, especially for binary and non-binary trans users.

A Network Selection Algorithm with Parallel Transmission Based on MADM

2014 ◽  
Vol 556-562 ◽  
pp. 4606-4611 ◽  
Author(s):  
Li Na Zhang ◽  
Qi Zhu
Keyword(s):  
Wireless Networks ◽  
Quality Of Service ◽  
Heterogeneous Wireless Networks ◽  
Network Selection ◽  
Selection Algorithm ◽  
Decision Matrix ◽  
Parallel Transmission ◽  
User Throughput ◽  
Selection Algorithms

At present, most of researches on network selection algorithms are focused on single access. To support seamless mobility and provide better quality of service in heterogeneous wireless networks, this paper proposes a network selection algorithm with parallel transmission based on MADM. In the algorithm, we firstly determine all available wireless networks and consider every subset of these networks as a network scheme. Then we obtain aggregation attributes of every scheme and determine the alternative network schemes. Finally, we build the decision matrix of multiple attributes and determine the optimal scheme by using GRA. Simulation results show that the proposed algorithm can obviously improve user quality of service, improve user throughput, reduce power consumption and price cost per bit.

scholarly journals The VAO Transient Facility

2011 ◽  
Vol 7 (S285) ◽  
pp. 318-320
Author(s):  
Matthew J. Graham ◽  
S. G. Djorgovski ◽  
Andrew Drake ◽  
Ashish Mahabal ◽  
Roy Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Time Domain ◽  
Astronomical Observatory ◽  
Sky Surveys ◽  
Prior Art ◽  
Event Notification ◽  
The Time Domain ◽  
Time Critical

AbstractThe time-domain community wants robust and reliable tools to enable the production of, and subscription to, community-endorsed event notification packets (VOEvent). The Virtual Astronomical Observatory (VAO) Transient Facility (VTF) is being designed to be the premier brokering service for the community, both collecting and disseminating observations about time-critical astronomical transients but also supporting annotations and the application of intelligent machine-learning to those observations. Two types of activity associated with the facility can therefore be distinguished: core infrastructure, and user services. We review the prior art in both areas, and describe the planned capabilities of the VTF. In particular, we focus on scalability and quality-of-service issues required by the next generation of sky surveys such as LSST and SKA.

scholarly journals A Survey on Machine Learning and Deep Learning based Quality of Service aware Protocols for Software Defined Networks

2021 ◽  
Author(s):  
Hiren Kumar Deva Sarma
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Deep Learning ◽  
Service Level Agreement ◽  
Service Level ◽  
Future Research ◽  
Traffic Classification ◽  
Software Defined Networks ◽  
Research Issues

<p>Quality of Service (QoS) is one of the most important parameters to be considered in computer networking and communication. The traditional network incorporates various quality QoS frameworks to enhance the quality of services. Due to the distributed nature of the traditional networks, providing quality of service, based on service level agreement (SLA) is a complex task for the network designers and administrators. With the advent of software defined networks (SDN), the task of ensuring QoS is expected to become feasible. Since SDN has logically centralized architecture, it may be able to provide QoS, which was otherwise extremely difficult in traditional network architectures. Emergence and popularity of machine learning (ML) and deep learning (DL) have opened up even more possibilities in the line of QoS assurance. In this article, the focus has been mainly on machine learning and deep learning based QoS aware protocols that have been developed so far for SDN. The functional areas of SDN namely traffic classification, QoS aware routing, queuing, and scheduling are considered in this survey. The article presents a systematic and comprehensive study on different ML and DL based approaches designed to improve overall QoS in SDN. Different research issues & challenges, and future research directions in the area of QoS in SDN are outlined. <b></b></p>

scholarly journals An Analysis of the Network Selection Problem for Heterogeneous Environments with User-Operator Joint Satisfaction and Multi-RAT Transmission

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
J. J. Escudero-Garzás ◽  
C. Bousoño-Calzón
Keyword(s):  
Wireless Networks ◽  
Quality Of Service ◽  
Heterogeneous Networks ◽  
Network Topology ◽  
Utility Functions ◽  
Selection Problem ◽  
Network Selection ◽  
Heterogeneous Environments ◽  
Radio Access

The trend in wireless networks is that several wireless radio access technologies (RATs) coexist in the same area, forming heterogeneous networks in which the users may connect to any of the available RATs. The problem of associating a user to the most suitable RAT, known as network selection problem (NSP), is of capital importance for the satisfaction of the users in these emerging environments. However, also the satisfaction of the operator is important in this scenario. In this work, we propose that a connection may be served by more than one RAT by using multi-RAT terminals. We formulate the NSP with multiple RAT association based on utility functions that take into consideration both user’s satisfaction and provider’s satisfaction. As users are characterized according to their expected quality of service, our results exhaustively analyze the influence of the user’s profile, along with the network topology and the type of applications served.

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