東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Quantifying Change Risk in Cloud Computing Environments.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantifying Change Risk in Cloud Computing Environments./
Author:	Alfred, Andre O.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	132 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
Subject:	Information technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28864343
ISBN:	9798759946373

Quantifying Change Risk in Cloud Computing Environments.
Alfred, Andre O.

Quantifying Change Risk in Cloud Computing Environments. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 132 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (D.Engr.)--The George Washington University, 2022.

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

This research introduces a System Dynamics model that engineers can use to simulate the expected value of economic damage to cloud customers if a change fails. As cloud adoption rapidly increases, the revenue loss potential of cloud customers due to human error can be significant. The model's parameters enables an engineer to incorporate a quantitatively derived worst-case impact from the customer's perspective. Typical technical change management processes in organizations are qualitative technical judgments of risk and implications based on the engineer's knowledge of making the change. It is unlikely that the engineer working on infrastructure components has any visibility of the change's economic risk. The practical application of this model focuses on simulating the probability of technical human errors relating to the compute portion of a typical cloud architecture. Engineers can quickly adapt the model for any layer of the cloud and any class of errors. .

ISBN: 9798759946373Subjects--Topical Terms:

532993
Information technology.
Subjects--Index Terms:

Worst-case impact

Quantifying Change Risk in Cloud Computing Environments.
LDR:02170nmm a2200385 4500 001 2344943
005 20220531062221.5
008 241004s2022 ||||||||||||||||| ||eng d
020 $a 9798759946373
035 $a (MiAaPQ)AAI28864343
035 $a AAI28864343
040 $a MiAaPQ $c MiAaPQ
100 1 $a Alfred, Andre O. $0 (orcid)0000-0002-9273-5746 $3 3683791
245 1 0 $a Quantifying Change Risk in Cloud Computing Environments.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 132 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
500 $a Advisor: Etemadi, Amir.
502 $a Thesis (D.Engr.)--The George Washington University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a This research introduces a System Dynamics model that engineers can use to simulate the expected value of economic damage to cloud customers if a change fails. As cloud adoption rapidly increases, the revenue loss potential of cloud customers due to human error can be significant. The model's parameters enables an engineer to incorporate a quantitatively derived worst-case impact from the customer's perspective. Typical technical change management processes in organizations are qualitative technical judgments of risk and implications based on the engineer's knowledge of making the change. It is unlikely that the engineer working on infrastructure components has any visibility of the change's economic risk. The practical application of this model focuses on simulating the probability of technical human errors relating to the compute portion of a typical cloud architecture. Engineers can quickly adapt the model for any layer of the cloud and any class of errors. .
590 $a School code: 0075.
650 4 $a Information technology. $3 532993
650 4 $a Industrial engineering. $3 526216
650 4 $a Systems science. $3 3168411
653 $a Worst-case impact
653 $a System dynamics
653 $a Economic damage
653 $a Cloud architecture
653 $a Change management
653 $a Human error
690 $a 0489
690 $a 0546
690 $a 0790
710 2 $a The George Washington University. $b Engineering Management. $3 1262973
773 0 $t Dissertations Abstracts International $g 83-06B.
790 $a 0075
791 $a D.Engr.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28864343