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Decentralized online clustering for supporting autonomic management of distributed systems

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TypeOfResource
Text
TitleInfo (ID = T-1)
Title
Decentralized online clustering for supporting autonomic management of distributed systems
Identifier
ETD_2603
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000053151
Language
LanguageTerm (authority = ISO639-2); (type = code)
eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (ID = SBJ-2); (authority = ETD-LCSH)
Topic
Distributed databases--Management
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
Autonomic computing
Abstract (type = abstract)
Distributed computational infrastructures, as well as the applications and services that they support, are increasingly becoming an integral part of society and affecting every aspect of life. As a result, ensuring their efficient and robust operation is critical. However, the scale and overall complexity of these systems is growing at an alarming rate (current data centers contain tens to hundreds of thousands of computing and storage devices running complex applications), making the management of these systems extremely challenging and rapidly exceeding human capability. The large quantities of distributed system data, in the form of user and component interaction and status events, contain meaningful information that can be used to infer the states of different components or of the system as a whole. Accurate and timely knowledge of these states is essential for verifying the correctness and efficiency of the operation of the system, as well as for discovering specific situations of interest, such as anomalies or faults, that require the application of appropriate management actions. Autonomic systems/applications must therefore be able to effectively process the large amounts of distributed data and to characterize operational states in a robust, accurate and timely manner. Although highly accurate, centralized approaches for distributed system management are infeasible in general because of the costs of centralization in terms of infrastructure, fault tolerance, and responsiveness. Since data is naturally distributed and the collective computing power of networked elements (ranging from sensor and device networks to supercomputer clusters and multi-organization grids) is enough to be harnessed for value added, system-level services, online and decentralized approaches for monitoring, data analysis, and self-management are not only feasible, but also quite attractive.This work is based on the premise of realizing and applying online data analysis, exploiting the collective computing resources of distributed systems for supporting autonomic management capabilities. Specifically, we propose and develop decentralized online clustering as a data analysis mechanism and infrastructure, evaluate its accuracy and performance with respect to other known clustering methods, and apply it to the following autonomic management problems: 1) System profiling and outlier detection from distributed data, 2) definition, autonomic adaptation, and application of management policies, and 3) VM provisioning and energy management in data centers.
PhysicalDescription
Form (authority = gmd)
electronic resource
Extent
xi, 137 p. : ill.
InternetMediaType
application/pdf
InternetMediaType
text/xml
Note (type = degree)
Ph.D.
Note
Includes abstract
Note
Vita
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Andres Quiroz Hernandez
Name (ID = NAME-1); (type = personal)
NamePart (type = family)
Quiroz Hernandez
NamePart (type = given)
Andres
Role
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author
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Andres Quiroz Hernandez
Name (ID = NAME-2); (type = personal)
NamePart (type = family)
Parashar
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Manish
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chair
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Advisory Committee
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Manish Parashar
Name (ID = NAME-3); (type = personal)
NamePart (type = family)
Gruteser
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Marco
Role
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internal member
Affiliation
Advisory Committee
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Marco Gruteser
Name (ID = NAME-4); (type = personal)
NamePart (type = family)
Pompili
NamePart (type = given)
Dario
Role
RoleTerm (authority = RULIB)
internal member
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Advisory Committee
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Dario Pompili
Name (ID = NAME-5); (type = personal)
NamePart (type = family)
Gnanasambandam
NamePart (type = given)
Nathan
Role
RoleTerm (authority = RULIB)
outside member
Affiliation
Advisory Committee
DisplayForm
Nathan Gnanasambandam
Name (ID = NAME-1); (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (ID = NAME-2); (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
OriginInfo
DateCreated (qualifier = exact)
2010
DateOther (qualifier = exact); (type = degree)
2010
Place
PlaceTerm (type = code)
xx
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T32J6BXR
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (AUTHORITY = GS); (ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder (ID = PRH-1); (type = personal)
Name
FamilyName
Quiroz Hernandez
GivenName
Andres
Role
Copyright Holder
RightsEvent (ID = RE-1); (AUTHORITY = rulib)
Type
Permission or license
DateTime
2010-04-14 13:17:11
AssociatedEntity (ID = AE-1); (AUTHORITY = rulib)
Role
Copyright holder
Name
Andres Quiroz Hernandez
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject (ID = AO-1); (AUTHORITY = rulib)
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.
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Technical

ContentModel
ETD
MimeType (TYPE = file)
application/pdf
MimeType (TYPE = container)
application/x-tar
FileSize (UNIT = bytes)
1945600
Checksum (METHOD = SHA1)
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