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Reinforcement Q-learning enabled energy-efficient service function chain provisioning in multi-domain networks

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Pubblicato in:Peer-To-Peer Networking and Applications vol. 18, no. 1 (Jan 2025), p. 58
Pubblicazione:
Springer Nature B.V.
Soggetti:
Network function virtualization
Machine learning
Collaboration
Success
Bandwidths
Decision making
Provisioning
Algorithms
Energy efficiency
Linear programming
Virtual networks
Privacy
Energy consumption
Cost control
Heuristic
Peer to peer computing
Heuristic methods
Profitability
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Abstract:Network function virtualization (NFV) technology is an efficient way to address the increasing difficulty of provisioning and managing network services. However, NFV-related service function chaining (SFC) deployment in multi-domain networks remains challenging, and there is still room for performance improvement. This paper investigates many heuristic algorithms in the same field and proposes a new method for dynamic SFC deployment in a multi-domain network. In our study, we combine a heuristic algorithm with reinforcement learning and divide the complex problem into several parts. This algorithm efficiently gives the SFC deployment scheme in the multi-domain network with subdomain privacy protection requirements and considers the energy savings of the multi-domain networks. Compared with the existing approach, the proposed algorithm has superiorities in terms of deployment success ratio, deployment profit, time efficiency, and energy consumption.
ISSN:1936-6442
1936-6450
DOI:10.1007/s12083-024-01861-1
Fonte:Science Database