Reinforcement Q-learning enabled energy-efficient service function chain provisioning in multi-domain networks
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| I publikationen: | Peer-To-Peer Networking and Applications vol. 18, no. 1 (Jan 2025), p. 58 |
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| Utgiven: |
Springer Nature B.V.
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| Ämnen: | |
| Länkar: | Citation/Abstract Full Text - PDF |
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MARC
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| 001 | 3203308302 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1936-6442 | ||
| 022 | |a 1936-6450 | ||
| 024 | 7 | |a 10.1007/s12083-024-01861-1 |2 doi | |
| 035 | |a 3203308302 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 084 | |a 108130 |2 nlm | ||
| 245 | 1 | |a Reinforcement Q-learning enabled energy-efficient service function chain provisioning in multi-domain networks | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Network function virtualization | ||
| 653 | |a Machine learning | ||
| 653 | |a Collaboration | ||
| 653 | |a Success | ||
| 653 | |a Bandwidths | ||
| 653 | |a Decision making | ||
| 653 | |a Provisioning | ||
| 653 | |a Algorithms | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Linear programming | ||
| 653 | |a Virtual networks | ||
| 653 | |a Privacy | ||
| 653 | |a Energy consumption | ||
| 653 | |a Cost control | ||
| 653 | |a Heuristic | ||
| 653 | |a Peer to peer computing | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Profitability | ||
| 773 | 0 | |t Peer-To-Peer Networking and Applications |g vol. 18, no. 1 (Jan 2025), p. 58 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3203308302/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3203308302/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |