Queen's University - Utility Bar

QSpace at Queen's University >
Graduate Theses, Dissertations and Projects >
Queen's Graduate Theses and Dissertations >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/1692

Authors: Thakkar, Dharmesh

Files in This Item:

File SizeFormat
Thakkar_Dharmesh_B_200901_MSc.pdf1.32 MBAdobe PDFView/Open
Keywords: capacity planning
fault resolution
customer support
event correlation
Issue Date: 2009
Series/Report no.: Canadian theses
Abstract: Capacity planning is crucial for successful development of enterprise applications. Capacity planning activities are most consequential during the verification and maintenance phases of Software Development Life Cycle. During the verification phase, analysts need to execute a large number of performance tests to build accurate performance models. Performance models help customers in capacity planning for their deployments. To build valid performance models, the performance tests must be redone for every release or build of an application. This is a time-consuming and error-prone manual process, which needs tools and techniques to speed up the process. In the maintenance phase, when customers run into performance and capacity related issues after deployment, they commonly engage the vendor of the application for troubleshooting and fine tuning of the troubled deployments. At the end of the engagement, analysts create engagement report, which contain valuable information about the observed symptoms, attempted workarounds, identified problems, and the final solutions. Engagement reports are stored in a customer engagement repository. While information stored in the engagement reports is valuable in helping analysts with future engagements, no systematic techniques exist to retrieve relevant reports from such a repository. In this thesis we present a framework for the systematic and automated building of capacity calculators during software verification phase. Then, we present a technique to retrieve relevant reports from a customer engagement repository. Our technique helps analyst fix performance and capacity related issues in the maintenance phase by providing easy access to information from relevant reports. We demonstrate our contributions with case studies on an open-source benchmarking application and an enterprise application.
Description: Thesis (Master, Computing) -- Queen's University, 2009-01-29 14:14:37.235
URI: http://hdl.handle.net/1974/1692
Appears in Collections:School of Computing Graduate Theses
Queen's Graduate Theses and Dissertations

Items in QSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


  DSpace Software Copyright © 2002-2008  The DSpace Foundation - TOP