東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Increasing Revenue by Applying Machi...

Jana, Nabarun.

FindBook

Google Book

Amazon

博客來

Increasing Revenue by Applying Machine Learning to Congestion Management in SDN.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Increasing Revenue by Applying Machine Learning to Congestion Management in SDN./
作者:	Jana, Nabarun.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	44 p.
附註:	Source: Masters Abstracts International, Volume: 80-07.
Contained By:	Masters Abstracts International80-07.
標題:	Computer Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13425043
ISBN:	9780438801578

Increasing Revenue by Applying Machine Learning to Congestion Management in SDN.
Jana, Nabarun.

Increasing Revenue by Applying Machine Learning to Congestion Management in SDN. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 44 p.

Source: Masters Abstracts International, Volume: 80-07.

Thesis (M.S.)--Rochester Institute of Technology, 2018.

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

With the advent of 5G, IoT and 4k videos, online gaming, movie streaming and other data intensive applications, the demand for data is sky rocketing. Due to this surge in data, the load on the network increases. This heightened network load causes degradation in network performance. Which can lead to the customer Service Provider (CSP)s loosing revenue if the Service Level Agreement (SLA) are not met. This report describes how machine learning techniques such as tit for tat can be applied to telecom networks. Machine learning applied to telecom networks help detect congestion and maintain SLAs while increasing yield (revenue). Several experiments are run with varying conditions on the network, such as low, medium and high loads; different levels of SLA for bandwidth and delay. Once the original conditions are tested without applying any smart blocking techniques, machine learning is applied to detect congestion in the network and block flows to maintain SLA and increase the number of flows that generate revenue.

ISBN: 9780438801578Subjects--Topical Terms:

1567821
Computer Engineering.

Increasing Revenue by Applying Machine Learning to Congestion Management in SDN.
LDR:02104nmm a2200337 4500 001 2264493
005 20200504070430.5
008 220629s2018 ||||||||||||||||| ||eng d
020 $a 9780438801578
035 $a (MiAaPQ)AAI13425043
035 $a (MiAaPQ)rit:13174
035 $a AAI13425043
040 $a MiAaPQ $c MiAaPQ
100 1 $a Jana, Nabarun. $3 3541615
245 1 0 $a Increasing Revenue by Applying Machine Learning to Congestion Management in SDN.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 44 p.
500 $a Source: Masters Abstracts International, Volume: 80-07.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Nygate, Joseph.
502 $a Thesis (M.S.)--Rochester Institute of Technology, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a With the advent of 5G, IoT and 4k videos, online gaming, movie streaming and other data intensive applications, the demand for data is sky rocketing. Due to this surge in data, the load on the network increases. This heightened network load causes degradation in network performance. Which can lead to the customer Service Provider (CSP)s loosing revenue if the Service Level Agreement (SLA) are not met. This report describes how machine learning techniques such as tit for tat can be applied to telecom networks. Machine learning applied to telecom networks help detect congestion and maintain SLAs while increasing yield (revenue). Several experiments are run with varying conditions on the network, such as low, medium and high loads; different levels of SLA for bandwidth and delay. Once the original conditions are tested without applying any smart blocking techniques, machine learning is applied to detect congestion in the network and block flows to maintain SLA and increase the number of flows that generate revenue.
590 $a School code: 0465.
650 4 $a Computer Engineering. $3 1567821
650 4 $a Engineering. $3 586835
650 4 $a Computer science. $3 523869
690 $a 0464
690 $a 0537
690 $a 0984
710 2 $a Rochester Institute of Technology. $b Telecommunications Engineering Technology. $3 1682676
773 0 $t Masters Abstracts International $g 80-07.
790 $a 0465
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13425043