Adaptive service function chaining mappings in 5G using deep Q-learning

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

Adaptive service function chaining mappings in 5G using deep Q-learning

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

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Computer Communications, 2020, 152, pp. 305-315
Issue Date:: 2020-02-15

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Field	Value	Language
dc.contributor.author	Li, G
dc.contributor.author	Feng, B
dc.contributor.author	Zhou, H
dc.contributor.author	Zhang, Y
dc.contributor.author	Sood, K
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2020-10-28T08:56:15Z
dc.date.available	2020-10-28T08:56:15Z
dc.date.issued	2020-02-15
dc.identifier.citation	Computer Communications, 2020, 152, pp. 305-315
dc.identifier.issn	0140-3664
dc.identifier.issn	1873-703X
dc.identifier.uri	http://hdl.handle.net/10453/143557
dc.description.abstract	© 2020 Elsevier B.V. With introduction of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, mobile network operators are able to provide on-demand Service Function Chaining (SFC) to meet various needs from users. However, it is challenging to map multiple SFCs to substrate networks efficiently, particularly in a number of key scenarios of forthcoming 5G, where user requests have different priorities and various resource demands. To this end, we first formulate the mapping of multiple SFCs with priorities as a multi-step Linear Integer Programming (ILP) problem, of which the mapping strategy (i.e., the objective function) in each step is configurable to improve overall CPU and bandwidth resource utilization rates. Secondly, to solve the strategy selection problem in each step and alleviate the complexity of ILP, we propose an adaptive deep Q-learning based SFC mapping approach (ADAP), where an agent is learned to make decisions from two low-complexity heuristic SFC mapping algorithms. Finally, we conduct extensive simulations using multiple SFC requests with randomly generated CPU and bandwidth demands in a real-world substrate network topology. Related results demonstrate that compared with a single strategy or random selections of strategies under the ILP-based approach or the proposed heuristic algorithms, our ADAP approach can improve whole-system resource efficiency by scheduling this two simply designed heuristic algorithms properly after limited training episodes.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Computer Communications
dc.relation.isbasedon	10.1016/j.comcom.2020.01.035
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Adaptive service function chaining mappings in 5G using deep Q-learning
dc.type	Journal Article
utslib.citation.volume	152
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-28T08:56:07Z
pubs.publication-status	Published
pubs.volume	152

Abstract:

© 2020 Elsevier B.V. With introduction of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, mobile network operators are able to provide on-demand Service Function Chaining (SFC) to meet various needs from users. However, it is challenging to map multiple SFCs to substrate networks efficiently, particularly in a number of key scenarios of forthcoming 5G, where user requests have different priorities and various resource demands. To this end, we first formulate the mapping of multiple SFCs with priorities as a multi-step Linear Integer Programming (ILP) problem, of which the mapping strategy (i.e., the objective function) in each step is configurable to improve overall CPU and bandwidth resource utilization rates. Secondly, to solve the strategy selection problem in each step and alleviate the complexity of ILP, we propose an adaptive deep Q-learning based SFC mapping approach (ADAP), where an agent is learned to make decisions from two low-complexity heuristic SFC mapping algorithms. Finally, we conduct extensive simulations using multiple SFC requests with randomly generated CPU and bandwidth demands in a real-world substrate network topology. Related results demonstrate that compared with a single strategy or random selections of strategies under the ILP-based approach or the proposed heuristic algorithms, our ADAP approach can improve whole-system resource efficiency by scheduling this two simply designed heuristic algorithms properly after limited training episodes.

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http://hdl.handle.net/10453/143557