Repeatable Multi-Dimensional Virtual Network Embedding in Cloud Service Platform

Zhang, W; Wang, D; Yu, S; He, H; Wang, Y

Repeatable Multi-Dimensional Virtual Network Embedding in Cloud Service Platform

Zhang, W Wang, D Yu, S

He, H Wang, Y

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Services Computing, 2022, 15, (6), pp. 3499-3512
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Zhang, W
dc.contributor.author	Wang, D
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	He, H
dc.contributor.author	Wang, Y
dc.date.accessioned	2023-04-05T04:05:44Z
dc.date.available	2023-04-05T04:05:44Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Services Computing, 2022, 15, (6), pp. 3499-3512
dc.identifier.issn	2372-0204
dc.identifier.issn	1939-1374
dc.identifier.uri	http://hdl.handle.net/10453/169173
dc.description.abstract	Virtual network embedding (VNE) can effectively deploy virtual networks (VNs) onto shared substrate network (SN) resources. However, with the consistent changing scalability and diversity demands of VNs, traditional VNE methods prove to be a challenging task for current cloud service platforms. Thus, we model a repeatable multi-dimensional virtual network embedding (RMD-VNE) problem for implementing multi-dimensional virtual networks (MD-VNs) that involves real servers, virtual machines, containers, and network simulators. The MD-VN is preprocessed and embedded via a heuristic method denoted as ReMiDvne. Following its transformation for the containers and simulation networks, the MD-VN topology undergoes a process of coarsening, partitioning, and uncoarsening. ReMiDvne then applies a topology-aware repeatable embedding solution to complete the embedding stage. Experimental results demonstrate that ReMiDvne outperforms seven baseline approaches through small-, 1,000- and 10,000-scale VNE simulation experiments. Remarkably, ReMiDvne improves the average rates of acceptance ratio, revenue, and revenue-cost ratio by up to 40.45%, 40.45%, and 299.03%, respectively, and reduces the average rate of cost by up to 64.16%. Furthermore, real-world VNE experiments are conducted based on the OpenStack platform. The results reveal the ability of ReMiDvne to efficiently reduce communication costs by up to 45.93% and 63.43% for download and upload, respectively.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Services Computing
dc.relation.isbasedon	10.1109/TSC.2021.3102016
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.title	Repeatable Multi-Dimensional Virtual Network Embedding in Cloud Service Platform
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
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 Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-04-05T04:05:42Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	6

Abstract:

Virtual network embedding (VNE) can effectively deploy virtual networks (VNs) onto shared substrate network (SN) resources. However, with the consistent changing scalability and diversity demands of VNs, traditional VNE methods prove to be a challenging task for current cloud service platforms. Thus, we model a repeatable multi-dimensional virtual network embedding (RMD-VNE) problem for implementing multi-dimensional virtual networks (MD-VNs) that involves real servers, virtual machines, containers, and network simulators. The MD-VN is preprocessed and embedded via a heuristic method denoted as ReMiDvne. Following its transformation for the containers and simulation networks, the MD-VN topology undergoes a process of coarsening, partitioning, and uncoarsening. ReMiDvne then applies a topology-aware repeatable embedding solution to complete the embedding stage. Experimental results demonstrate that ReMiDvne outperforms seven baseline approaches through small-, 1,000- and 10,000-scale VNE simulation experiments. Remarkably, ReMiDvne improves the average rates of acceptance ratio, revenue, and revenue-cost ratio by up to 40.45%, 40.45%, and 299.03%, respectively, and reduces the average rate of cost by up to 64.16%. Furthermore, real-world VNE experiments are conducted based on the OpenStack platform. The results reveal the ability of ReMiDvne to efficiently reduce communication costs by up to 45.93% and 63.43% for download and upload, respectively.

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http://hdl.handle.net/10453/169173