Mapping and Scheduling of Virtual Network Functions using Multi Objective Optimization Algorithm

Gamal, M; Abolhasan, M; Jafarizadeh, S; Lipman, J; Ni, W

Mapping and Scheduling of Virtual Network Functions using Multi Objective Optimization Algorithm

Gamal, M Abolhasan, M

Jafarizadeh, S Lipman, J

Ni, W

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2019 19th International Symposium on Communications and Information Technologies, ISCIT 2019, 2019, pp. 328 - 333
Issue Date:: 2019-09-01

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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	Jafarizadeh, S	en_US
dc.contributor.author	Lipman, J https://orcid.org/0000-0003-2877-1168	en_US
dc.contributor.author	Ni, W	en_US
dc.date.accessioned	2020-04-07T14:38:49Z
dc.date.available	2020-04-07T14:38:49Z
dc.date.issued	2019-09-01	en_US
dc.identifier.citation	Proceedings - 2019 19th International Symposium on Communications and Information Technologies, ISCIT 2019, 2019, pp. 328 - 333	en_US
dc.identifier.isbn	9781728150093	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139873
dc.description.abstract	© 2019 IEEE. Within the context of Software-Defined Networking (SDN), the problem of resource allocation for a set of incoming Virtual Network Functions (VNF) service requests has been the focus of many studies. In this paper, a new optimization model has been developed to find the near to optimal mapping and scheduling for the incoming VNF service requests. This model while considering delay, aims to achieve three objectives functions, namely, minimizing the transmission delays occurring in every link, minimizing the processing capacity for every Virtual Machine (VM) and minimizing the processing delay at every VM. The resultant problem is formulated as a multi-objective optimization problem and the developed solution is based on a multi-objective evolutionary algorithm utilizing the decomposition algorithm. Simulation results illustrate that the resulting algorithm is scalable while considering delay and it outperforms the genetic bandwidth link allocation (GA-BA) and genetic non-bandwidth link allocation (GA-NBA) algorithms.	en_US
dc.relation.ispartof	Proceedings - 2019 19th International Symposium on Communications and Information Technologies, ISCIT 2019	en_US
dc.relation.isbasedon	10.1109/ISCIT.2019.8905113	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Mapping and Scheduling of Virtual Network Functions using Multi Objective Optimization Algorithm	en_US
dc.type	Conference Proceeding
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
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2019 IEEE. Within the context of Software-Defined Networking (SDN), the problem of resource allocation for a set of incoming Virtual Network Functions (VNF) service requests has been the focus of many studies. In this paper, a new optimization model has been developed to find the near to optimal mapping and scheduling for the incoming VNF service requests. This model while considering delay, aims to achieve three objectives functions, namely, minimizing the transmission delays occurring in every link, minimizing the processing capacity for every Virtual Machine (VM) and minimizing the processing delay at every VM. The resultant problem is formulated as a multi-objective optimization problem and the developed solution is based on a multi-objective evolutionary algorithm utilizing the decomposition algorithm. Simulation results illustrate that the resulting algorithm is scalable while considering delay and it outperforms the genetic bandwidth link allocation (GA-BA) and genetic non-bandwidth link allocation (GA-NBA) algorithms.

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http://hdl.handle.net/10453/139873