Multi objective resource optimisation for network function virtualisation requests

Gamal, M; Abolhasan, M; Lipman, J; Liu, RP; Ni, W

Multi objective resource optimisation for network function virtualisation requests

Gamal, M Abolhasan, M

Lipman, J

Liu, RP

Ni, W

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Publication Type:: Conference Proceeding
Citation:: 26th International Conference on Systems Engineering, ICSEng 2018 - Proceedings, 2019
Issue Date:: 2019-02-08

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Field	Value	Language
dc.contributor.author	Gamal, M	en_US
dc.contributor.author	Abolhasan, M https://orcid.org/0000-0002-4282-6666	en_US
dc.contributor.author	Lipman, J https://orcid.org/0000-0003-2877-1168	en_US
dc.contributor.author	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X	en_US
dc.date.issued	2019-02-08	en_US
dc.identifier.citation	26th International Conference on Systems Engineering, ICSEng 2018 - Proceedings, 2019	en_US
dc.identifier.isbn	9781538678343	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134641
dc.description.abstract	© 2018 IEEE. Network function vitalization (NFV) as a new research concept, for both academia and industry, faces many challenges to network operators before it can be accepted into mainstream. One challenge addressed in this paper is to find the optimal placement f or a set of incoming requests with VNF service chains to serve in suitable Virtual Machines (VMs) such that a set of conflicting objectives are met. Mainly, focus is placed on maximizing the total saving cost by increasing the total CPU utilization during the processing time and increasing the processing time for every service request in the cloud network. Moreover, we aim to maximize the admitted traffic simultaneously while considering the system constraints. We formulate the problem as a multi-objective optimization problem and use a Resource Utilization Multi-Objective Evolutionary Algorithm based on Decomposition (RU-MOEA/D) algorithm to solve the problem considering the two objectives simultaneously. Extensive simulations are carried out to evaluate the effects of the different network sizes, genetic parameters and the number of server resources on the acceptable ratio of the arrival chains to serve in the available VMs. The empirical results illustrate that the proposed algorithm can solve the problem efficiently and compute the optimal solution for two objectives together within a reasonable running time.	en_US
dc.relation.ispartof	26th International Conference on Systems Engineering, ICSEng 2018 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICSENG.2018.8638192	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Multi objective resource optimisation for network function virtualisation requests	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. Network function vitalization (NFV) as a new research concept, for both academia and industry, faces many challenges to network operators before it can be accepted into mainstream. One challenge addressed in this paper is to find the optimal placement f or a set of incoming requests with VNF service chains to serve in suitable Virtual Machines (VMs) such that a set of conflicting objectives are met. Mainly, focus is placed on maximizing the total saving cost by increasing the total CPU utilization during the processing time and increasing the processing time for every service request in the cloud network. Moreover, we aim to maximize the admitted traffic simultaneously while considering the system constraints. We formulate the problem as a multi-objective optimization problem and use a Resource Utilization Multi-Objective Evolutionary Algorithm based on Decomposition (RU-MOEA/D) algorithm to solve the problem considering the two objectives simultaneously. Extensive simulations are carried out to evaluate the effects of the different network sizes, genetic parameters and the number of server resources on the acceptable ratio of the arrival chains to serve in the available VMs. The empirical results illustrate that the proposed algorithm can solve the problem efficiently and compute the optimal solution for two objectives together within a reasonable running time.

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http://hdl.handle.net/10453/134641