<div class="csl-bib-body">
<div class="csl-entry">Borkowski, M. (2020). <i>Predictive approaches for resource provisioning in distributed systems</i> [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.83025</div>
</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2020.83025
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/15472
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dc.description.abstract
Modern distributed systems, such as cloud computing infrastructures or data stream processing engines, perform resource provisioning tasks such as resource allocation, task scheduling, or scaling. This decision-making substantially influences the systems’ performance, and therefore, the manner of reaching these decisions is crucial to the systems’ operation with regard to cost efficiency, performance, reliability, and adherence to service level agreements. Currently, many approaches to resource provisioning in distributed systems are reactive, i.e., they measure the systems’ state, analyze it, and perform necessary actions. The main downside of reactive approaches is that effectively, such systems perform resource provisioning based only on past observations. In a highly dynamic environment with rapidly changing demands for computational resources, this can lead to delayed reactions, which increase cost, degrade performance, and reduce reliability. This thesis proposes the use of predictive technologies for performing resource provisioning tasks in modern distributed systems. As a foundation, methods stemming from research in the field of machine learning are used to improve target metrics like system performance oroperational cost. In contrast to traditional, reactive approaches, the proposed methodology of predictive decision-making is able to perform operational tasks ahead of time, such as scaling out in advance for a predicted increase of demand. We show how to use predictive methods in various domains of distributed systems, namely cloud computing, business process management systems, data stream processing, and blockchains. We propose approaches to solving challenges in designing predictive methods, such as metric prediction, failure prediction, or data filtering and estimation. We evaluate the impact of the proposed methods on the system using various quantitative methods, including testbed evaluation and simulation, as well as formal and qualitative analysis. Our results show that employing predictive approaches in these domains of distributed systems significantly improves performance attributes such as response time or adherence to service level agreements.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Predictive Systems
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dc.subject
Machine Learning
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dc.subject
Cloud Computing
en
dc.subject
Business Processes
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dc.subject
Resource Provisioning
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dc.subject
Data Stream Processing
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dc.subject
Blockchain
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dc.title
Predictive approaches for resource provisioning in distributed systems
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dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2020.83025
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Michael Borkowski
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E194 - Institut für Information Systems Engineering
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dc.type.qualificationlevel
Doctoral
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dc.identifier.libraryid
AC15746367
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dc.description.numberOfPages
165
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dc.thesistype
Dissertation
de
dc.thesistype
Dissertation
en
tuw.author.orcid
0000-0003-3440-8592
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dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.advisor.orcid
0000-0001-6828-9945
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item.openaccessfulltext
Open Access
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item.grantfulltext
open
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item.cerifentitytype
Publications
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item.mimetype
application/pdf
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item.openairecristype
http://purl.org/coar/resource_type/c_db06
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item.languageiso639-1
en
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item.openairetype
doctoral thesis
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item.fulltext
with Fulltext
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crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering