Efficient Provision of Service Function Chains in Overlay Networks using Reinforcement Learning

Li, G; Zhou, H; Feng, B; Zhang, Y; Yu, S

Efficient Provision of Service Function Chains in Overlay Networks using Reinforcement Learning

Li, G Zhou, H Feng, B Zhang, Y Yu, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cloud Computing, 2021, PP, (99), pp. 1-1
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Li, G
dc.contributor.author	Zhou, H
dc.contributor.author	Feng, B
dc.contributor.author	Zhang, Y
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2021-04-28T07:50:09Z
dc.date.available	2021-04-28T07:50:09Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Transactions on Cloud Computing, 2021, PP, (99), pp. 1-1
dc.identifier.issn	2168-7161
dc.identifier.issn	2168-7161
dc.identifier.uri	http://hdl.handle.net/10453/148499
dc.description.abstract	IEEE Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies facilitate deploying Service Function Chains (SFCs) at clouds in efficiency and flexibility. However, it is still challenging to efficiently chain Virtualized Network Functions (VNFs) in overlay networks without knowledge of underlying network configurations. Although there are many deterministic approaches for VNF placement and chaining, they have high complexity and depend on state information of substrate networks. Fortunately, Reinforcement Learning (RL) brings opportunities to alleviate this challenge as it can learn to make suitable decisions without prior knowledge. Therefore, in this paper, we propose an RL approach for efficient SFC provision in overlay networks, where the same VNFs provided by multiple vendors are with different performance. Specifically, we first formulate the problem into an Integer Linear Programming (ILP) model for benchmarking. Then, we present the online SFC path selection into a Markov Decision Process (MDP) and propose a corresponding policy-gradient-based solution. Finally, we evaluate our proposed approach with extensive simulations with randomly generated SFC requests and a real-world video streaming dataset, and implement an emulation system for feasibility verification. Related results demonstrate that performance of our approach is close to the ILP-based method and better than deep Q-learning, random, and load-least-greedy methods.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Cloud Computing
dc.relation.isbasedon	10.1109/TCC.2019.2961093
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0806 Information Systems
dc.title	Efficient Provision of Service Function Chains in Overlay Networks using Reinforcement Learning
dc.type	Journal Article
utslib.citation.volume	PP
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	true
dc.date.updated	2021-04-28T07:50:08Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

IEEE Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies facilitate deploying Service Function Chains (SFCs) at clouds in efficiency and flexibility. However, it is still challenging to efficiently chain Virtualized Network Functions (VNFs) in overlay networks without knowledge of underlying network configurations. Although there are many deterministic approaches for VNF placement and chaining, they have high complexity and depend on state information of substrate networks. Fortunately, Reinforcement Learning (RL) brings opportunities to alleviate this challenge as it can learn to make suitable decisions without prior knowledge. Therefore, in this paper, we propose an RL approach for efficient SFC provision in overlay networks, where the same VNFs provided by multiple vendors are with different performance. Specifically, we first formulate the problem into an Integer Linear Programming (ILP) model for benchmarking. Then, we present the online SFC path selection into a Markov Decision Process (MDP) and propose a corresponding policy-gradient-based solution. Finally, we evaluate our proposed approach with extensive simulations with randomly generated SFC requests and a real-world video streaming dataset, and implement an emulation system for feasibility verification. Related results demonstrate that performance of our approach is close to the ILP-based method and better than deep Q-learning, random, and load-least-greedy methods.

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http://hdl.handle.net/10453/148499