Automated Decision Making Methods for the Multi-objective Optimization Task
of Cloud Service Placement
M. Seufert, S. Lange, and M. Meixner. Programmability for Cloud Networks and Applications 2016 (PROCON 2016), Würzburg, Germany, (September 2016)
Abstract
The network functions virtualization (NFV) paradigm provides advantages
with respect to aspects like flexibility, costs, and scalability of
networks. However, management and orchestration of the resulting networks
also introduce new challenges. The placement of services and virtualized
network functions (VNFs) is a multi-objective optimization task that
confronts operators with a multitude of possible solutions that are
incomparable among each other. The goal of this work is to investigate
mechanisms that enable automated decision making between such multi
dimensional solutions. To this end, we investigate techniques from the
domain of multi attribute decision making that aggregate the performance of
placements to a single numeric score. A comparison between resulting
rankings of placements shows that many techniques produce similar results.
Hence, placements that achieve good rankings according to many approaches
might be viable candidates in the context of automated decision making.
%0 Conference Paper
%1 Seufert2016
%A Seufert, Michael
%A Lange, Stanislav
%A Meixner, Markus
%B Programmability for Cloud Networks and Applications 2016 (PROCON 2016)
%C Würzburg, Germany
%D 2016
%K itc itc28
%T Automated Decision Making Methods for the Multi-objective Optimization Task
of Cloud Service Placement
%U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc28/Seufert2016.pdf?inline=true
%X The network functions virtualization (NFV) paradigm provides advantages
with respect to aspects like flexibility, costs, and scalability of
networks. However, management and orchestration of the resulting networks
also introduce new challenges. The placement of services and virtualized
network functions (VNFs) is a multi-objective optimization task that
confronts operators with a multitude of possible solutions that are
incomparable among each other. The goal of this work is to investigate
mechanisms that enable automated decision making between such multi
dimensional solutions. To this end, we investigate techniques from the
domain of multi attribute decision making that aggregate the performance of
placements to a single numeric score. A comparison between resulting
rankings of placements shows that many techniques produce similar results.
Hence, placements that achieve good rankings according to many approaches
might be viable candidates in the context of automated decision making.
@inproceedings{Seufert2016,
abstract = {The network functions virtualization (NFV) paradigm provides advantages
with respect to aspects like flexibility, costs, and scalability of
networks. However, management and orchestration of the resulting networks
also introduce new challenges. The placement of services and virtualized
network functions (VNFs) is a multi-objective optimization task that
confronts operators with a multitude of possible solutions that are
incomparable among each other. The goal of this work is to investigate
mechanisms that enable automated decision making between such multi
dimensional solutions. To this end, we investigate techniques from the
domain of multi attribute decision making that aggregate the performance of
placements to a single numeric score. A comparison between resulting
rankings of placements shows that many techniques produce similar results.
Hence, placements that achieve good rankings according to many approaches
might be viable candidates in the context of automated decision making.},
added-at = {2016-08-31T16:30:53.000+0200},
address = {Würzburg, Germany},
author = {Seufert, Michael and Lange, Stanislav and Meixner, Markus},
biburl = {https://www.bibsonomy.org/bibtex/26b9010431f2e14a04815b252e8e42b71/itc},
booktitle = {Programmability for Cloud Networks and Applications 2016 (PROCON 2016)},
days = {12},
interhash = {755533679a1e2edbb4cf79dc395ec945},
intrahash = {6b9010431f2e14a04815b252e8e42b71},
keywords = {itc itc28},
month = {Sept},
timestamp = {2020-05-26T16:53:35.000+0200},
title = {Automated Decision Making Methods for the Multi-objective Optimization Task
of Cloud Service Placement},
url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc28/Seufert2016.pdf?inline=true},
year = 2016
}