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Improving application infrastructure provisioning using resource usage predictions from cloud metric data analysis

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TitleInfo
Title
Improving application infrastructure provisioning using resource usage predictions from cloud metric data analysis
Name (type = personal)
NamePart (type = family)
Hariharasubramanian
NamePart (type = given)
Mahesh
NamePart (type = date)
1992-
DisplayForm
Mahesh Hariharasubramanian
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Striki
NamePart (type = given)
Maria
DisplayForm
Maria Striki
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Petropulu
NamePart (type = given)
Athina
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Athina Petropulu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Chen
NamePart (type = given)
Yingying
DisplayForm
Yingying Chen
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2018
DateOther (qualifier = exact); (type = degree)
2018-10
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
There has been a huge interest by companies to utilize the cloud for their day-to-day operations. Cloud providers like AWS, Microsoft Azure, Google have been quite successful in serving its ever-increasing customer base. It is interesting to study how these companies use the cloud metrics to efficiently schedule their customers’ jobs and thereby utilize the shared infrastructure effectively. A lot of research has been done with the Google cloud cluster data released publicly in 2011 to analyze the task and job failure rates and predict failures thereby optimizing the resource utilization by smart scheduling techniques. 6 years from then, Microsoft Azure has also released their VM CPU utilization data publicly in October 2017 along with the SOSP 2017 paper called “Resource Central”. We will be one of the first to analyze this data set. In this work, we analyze this data and try to answer the following questions:
1. What are the VM CPU usage patterns by Azure subscribers?
2. Can we predict the future usage if yes, how and who all can benefit from this data?
3. Which techniques among statistical machine learning and deep learning are most suited to the Microsoft Azure data?
4. Can the learning models so formed be generalized for other similar data sets and problems like anomaly detection using log analysis at the application level?
5. How can these models augment the performance of existing VM scheduling algorithms?
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = ETD-LCSH)
Topic
Web services
Subject (authority = ETD-LCSH)
Topic
Cloud computing
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_9292
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (68 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Mahesh Hariharasubramanian
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-y8e4-5v69
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Hariharasubramanian
GivenName
Mahesh
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-10-03 03:39:26
AssociatedEntity
Name
Mahesh Hariharasubramanian
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2018-10-03T03:36:43
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2018-10-03T03:36:43
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