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Efficient Service for Next Generation Network Slicing Architecture and Mobile Traffic Analysis Using Machine Learning Technique.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Efficient Service for Next Generation Network Slicing Architecture and Mobile Traffic Analysis Using Machine Learning Technique./
Author:	Tapan, Billian Khan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	76 p.
Notes:	Source: Masters Abstracts International, Volume: 83-02.
Contained By:	Masters Abstracts International83-02.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28648187
ISBN:	9798535512471

Efficient Service for Next Generation Network Slicing Architecture and Mobile Traffic Analysis Using Machine Learning Technique.
Tapan, Billian Khan.

Efficient Service for Next Generation Network Slicing Architecture and Mobile Traffic Analysis Using Machine Learning Technique. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 76 p.

Source: Masters Abstracts International, Volume: 83-02.

Thesis (M.S.)--Marquette University, 2021.

This item must not be sold to any third party vendors.

The tremendous growth of mobile devices, IOT devices, applications and many other services have placed high demand on mobile and wireless network infrastructures. Much research and development of 5G mobile networks have found the way to support the huge volume of traffic, extracting of fine-gained analytics and agile management of mobile network elements, so that it can maximize the user experience. It is very challenging to accomplish the tasks as mobile networks increase the complexity, due to increases in the high volume of data penetration, devices, and applications. One of the solutions, advance machine learning techniques, can help to mitigate the large number of data and algorithm driven applications. This work mainly focus on extensive analysis of mobile traffic for improving the performance, key performance indicators and quality of service from the operations perspective. The work includes the collection of datasets and log files using different kind of tools in different network layers and implementing the machine learning techniques to analyze the datasets to predict mobile traffic activity. A wide range of algorithms were implemented to compare the analysis in order to identify the highest performance. Moreover, this thesis also discusses about network slicing architecture its use cases and how to efficiently use network slicing to meet distinct demands.

ISBN: 9798535512471Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Next generation

Efficient Service for Next Generation Network Slicing Architecture and Mobile Traffic Analysis Using Machine Learning Technique.
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