Toward efficient parallel routing optimization for large-scale SDN networks using GPGPU

Wang, X; Zhang, Q; Ren, J; Xu, S; Wang, S; Yu, S

Toward efficient parallel routing optimization for large-scale SDN networks using GPGPU

Wang, X Zhang, Q Ren, J Xu, S Wang, S Yu, S

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Publisher:: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Journal of Network and Computer Applications, 2018, 113, pp. 1-13
Issue Date:: 2018-07-01

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Field	Value	Language
dc.contributor.author	Wang, X
dc.contributor.author	Zhang, Q
dc.contributor.author	Ren, J
dc.contributor.author	Xu, S
dc.contributor.author	Wang, S
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2021-04-29T00:06:53Z
dc.date.available	2021-04-29T00:06:53Z
dc.date.issued	2018-07-01
dc.identifier.citation	Journal of Network and Computer Applications, 2018, 113, pp. 1-13
dc.identifier.issn	1084-8045
dc.identifier.issn	1095-8592
dc.identifier.uri	http://hdl.handle.net/10453/148520
dc.description.abstract	Routing optimization is an efficient way to improve network performance and guarantee the QoS requirements of users. However, with the rapid growth of network size and traffic demands, the routing optimization of SDN networks with centralized control plane is facing the scalability issue. To overcome the scalability issue, we aim to speed up the routing optimization process in large networks by utilizing the massive parallel computation capability of GPU. In this paper, we develop an efficient Lagrangian Relaxation based Parallel Routing Optimization Algorithm (LR-PROA). LR-PROA first decomposes the routing optimization problem into a set of path calculation problems for the traffic demands by relaxing the link capacity constraints, then the path calculation tasks are dispatched to GPU and executed concurrently on GPU. In order to achieve high degree of parallelism, LR-PROA also parallelizes the path calculation process for each traffic demand. Furthermore, to improve the convergence speed, LR-PROA uses efficient methods to adjust the calculated paths for a part of traffic demands and set the step size of subgradient algorithm for solving the Lagrangian dual problem in each iteration. Our evaluations on synthetic network topologies verify that LR-PROA has good optimization performance as well as superior calculation time efficiency. In our simulations, LR-PROA is up to tens of times faster than benchmark algorithms in large networks.
dc.language	English
dc.publisher	ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.relation.ispartof	Journal of Network and Computer Applications
dc.relation.isbasedon	10.1016/j.jnca.2018.03.031
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Toward efficient parallel routing optimization for large-scale SDN networks using GPGPU
dc.type	Journal Article
utslib.citation.volume	113
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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	*
dc.date.updated	2021-04-29T00:06:49Z
pubs.publication-status	Published
pubs.volume	113

Abstract:

Routing optimization is an efficient way to improve network performance and guarantee the QoS requirements of users. However, with the rapid growth of network size and traffic demands, the routing optimization of SDN networks with centralized control plane is facing the scalability issue. To overcome the scalability issue, we aim to speed up the routing optimization process in large networks by utilizing the massive parallel computation capability of GPU. In this paper, we develop an efficient Lagrangian Relaxation based Parallel Routing Optimization Algorithm (LR-PROA). LR-PROA first decomposes the routing optimization problem into a set of path calculation problems for the traffic demands by relaxing the link capacity constraints, then the path calculation tasks are dispatched to GPU and executed concurrently on GPU. In order to achieve high degree of parallelism, LR-PROA also parallelizes the path calculation process for each traffic demand. Furthermore, to improve the convergence speed, LR-PROA uses efficient methods to adjust the calculated paths for a part of traffic demands and set the step size of subgradient algorithm for solving the Lagrangian dual problem in each iteration. Our evaluations on synthetic network topologies verify that LR-PROA has good optimization performance as well as superior calculation time efficiency. In our simulations, LR-PROA is up to tens of times faster than benchmark algorithms in large networks.

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http://hdl.handle.net/10453/148520