<div class="csl-bib-body">
<div class="csl-entry">Rohrer, F. (2018). <i>Multi-objective user and virtual machine assignment using biogeography-based optimization</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2018.54569</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2018.54569
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/1823
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dc.description.abstract
null
de
dc.description.abstract
Applications such as vehicle routing systems, smart city applications and augmented reality games are new and demanding applications that have very low latency requirements. Fog Computing is a newcomputing paradigm that will pave theway for these applications. To that end, service providers will deploy their hardware (gateways, routers, servers) outside of data centers in order to move their infrastructure as close as possible to their users. In order to get benefit from the infrastructure, users have to be assigned to the right servers, depending on their physical location. Simultaneously, the users’ applications, have to be assigned to the same servers, ideally such that they are in close proximity to their users. The different objectives of both service providers and users, as well as physical limitations and constraints, however, make this assignment task a challenging problem. We capture user and service provider goals by six different objective functions (user distance, power consumption, resource waste, failure probability, reachability, user evenness). We use Biogeography-based optimization (BBO), a kind of genetic algorithm inspired by nature, to find solutions that simultaneously minimize all these functions in a multiobjective Pareto approach. As exact solutions are hard to obtain, we compare BBO against a greedy algorithm and the Genetic algorithm (GA). Our extensive simulations suggest that BBO is indeed applicable to find reasonably good solutions, however results vary upon the used objective function. BBO generally outperforms the greedy algorithm in almost all of the instances and often delivers slightly better results than GA as well, especially for smaller instance sizes. The work shows that BBO is an admissible approach for solving this assignment problem encountered in Fog Computing which can lead to lower latency for user applications and cost savings and higher availability guarantees for service providers.
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
Resource allocation
de
dc.subject
Edge Computing
de
dc.subject
Resource Allocation efficiency
de
dc.subject
Genetic algorithms
de
dc.subject
Resource allocation
en
dc.subject
Edge Computing
en
dc.subject
Resource Allocation efficiency
en
dc.subject
Genetic algorithms
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dc.title
Multi-objective user and virtual machine assignment using biogeography-based optimization
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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.2018.54569
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Florian Rohrer
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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