Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee
Network Function Virtualization (NFV) is an approach that provides a network service provider with agility and cost-efficiency in managing 6G network services. Standard traffic engineering rules are known limited in assuring a very stringent delay requirement in NFV when a traffic flow is required t...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9526552/ |
| _version_ | 1818740031662063616 |
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| author | Tuan-Minh Pham |
| author_facet | Tuan-Minh Pham |
| author_sort | Tuan-Minh Pham |
| collection | DOAJ |
| description | Network Function Virtualization (NFV) is an approach that provides a network service provider with agility and cost-efficiency in managing 6G network services. Standard traffic engineering rules are known limited in assuring a very stringent delay requirement in NFV when a traffic flow is required to follow a sequence of network functions scattered in data center networks. This paper proposes an innovative model and algorithm of traffic engineering for service function chaining (SFC) to maximize cost-efficiency under a delay-guarantee constraint. We first formulate the problem as a mixed-integer linear programming model for obtaining the optimal solution. We then propose an algorithm based on the reinforcement learning principles for finding an approximation solution in a large-scale problem with the dynamics of service demands. Numerical results under both real-world datasets and synthetic network topologies demonstrate that our proposed model and algorithm allow an NFV service provider (NSP) to place a virtual network function and steer a traffic flow efficiently in terms of energy cost for a delay-guarantee SFC. Importantly, the results provide an insight into the optimal and approximation solutions for an NSP to select a suitable traffic engineering approach with regard to network dynamics. |
| first_indexed | 2024-12-18T01:34:16Z |
| format | Article |
| id | doaj.art-a81048d3f14e443eb270222941dd65f6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T01:34:16Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a81048d3f14e443eb270222941dd65f62022-12-21T21:25:30ZengIEEEIEEE Access2169-35362021-01-01912158312159210.1109/ACCESS.2021.31092699526552Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay GuaranteeTuan-Minh Pham0https://orcid.org/0000-0001-9994-5773Faculty of Computer Science, Phenikaa University, Hanoi, VietnamNetwork Function Virtualization (NFV) is an approach that provides a network service provider with agility and cost-efficiency in managing 6G network services. Standard traffic engineering rules are known limited in assuring a very stringent delay requirement in NFV when a traffic flow is required to follow a sequence of network functions scattered in data center networks. This paper proposes an innovative model and algorithm of traffic engineering for service function chaining (SFC) to maximize cost-efficiency under a delay-guarantee constraint. We first formulate the problem as a mixed-integer linear programming model for obtaining the optimal solution. We then propose an algorithm based on the reinforcement learning principles for finding an approximation solution in a large-scale problem with the dynamics of service demands. Numerical results under both real-world datasets and synthetic network topologies demonstrate that our proposed model and algorithm allow an NFV service provider (NSP) to place a virtual network function and steer a traffic flow efficiently in terms of energy cost for a delay-guarantee SFC. Importantly, the results provide an insight into the optimal and approximation solutions for an NSP to select a suitable traffic engineering approach with regard to network dynamics.https://ieeexplore.ieee.org/document/9526552/Network function virtualizationoptimizationreinforcement learningservice function chainingtraffic engineering |
| spellingShingle | Tuan-Minh Pham Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee IEEE Access Network function virtualization optimization reinforcement learning service function chaining traffic engineering |
| title | Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee |
| title_full | Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee |
| title_fullStr | Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee |
| title_full_unstemmed | Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee |
| title_short | Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee |
| title_sort | traffic engineering based on reinforcement learning for service function chaining with delay guarantee |
| topic | Network function virtualization optimization reinforcement learning service function chaining traffic engineering |
| url | https://ieeexplore.ieee.org/document/9526552/ |
| work_keys_str_mv | AT tuanminhpham trafficengineeringbasedonreinforcementlearningforservicefunctionchainingwithdelayguarantee |