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A Genetic Algorithm based Approach for Change Management in Enterprise IT Systems: Optimal Change Scheduling

by Habib-ur-rehman, Irshad Khan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 3
Year of Publication: 2012
Authors: Habib-ur-rehman, Irshad Khan
10.5120/8026-1203

Habib-ur-rehman, Irshad Khan . A Genetic Algorithm based Approach for Change Management in Enterprise IT Systems: Optimal Change Scheduling. International Journal of Computer Applications. 51, 3 ( August 2012), 38-42. DOI=10.5120/8026-1203

@article{ 10.5120/8026-1203,
author = { Habib-ur-rehman, Irshad Khan },
title = { A Genetic Algorithm based Approach for Change Management in Enterprise IT Systems: Optimal Change Scheduling },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 3 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume51/number3/8026-1203/ },
doi = { 10.5120/8026-1203 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:49:29.136026+05:30
%A Habib-ur-rehman
%A Irshad Khan
%T A Genetic Algorithm based Approach for Change Management in Enterprise IT Systems: Optimal Change Scheduling
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 3
%P 38-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we provide a Genetic Algorithm (GA) based approach for change management in Enterprise IT systems which minimizes change delay and improves change capacity. Change management evolves the implementation of change queues on the set of applications which are running on one or more servers. To implement these changes there are some constraints involved: atomic nature of changes; when a change is implemented then it cannot be interrupted; an application has a specific downtime, which causes timing constraint; applications that share same resources such as servers have overlapping downtime which causes conflicts among changes. In such complex system, scheduling of changes becomes a difficult optimization task. GA has the ability to optimize such constraint scheduling problems. Our GA base scheduling improves throughput and minimizes change delay of existing Capacity optimal Fluid Scheduling algorithm.

References
  1. P. Kumar et al. , "Change Management in Enterprise IT Systems: Process Modeling and Capacity-optimal Scheduling", Proc. IEEE INFOCOM 2010.
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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithms Optimal Change Scheduling Enterprise IT Systems Fluid Scheduling

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