Network Traffic Prediction for Multiple Providers in Digital Twin-Assisted NFV-Enabled Network
محفوظ في:
| الحاوية / القاعدة: | Electronics vol. 14, no. 20 (2025), p. 4129-4156 |
|---|---|
| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , |
| منشور في: |
MDPI AG
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| الوسوم: |
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| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14204129 |2 doi | |
| 035 | |a 3265899048 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Hu, Ying | |
| 245 | 1 | |a Network Traffic Prediction for Multiple Providers in Digital Twin-Assisted NFV-Enabled Network | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This manuscript investigates the network traffic prediction problem, with the aim of predicting network traffic on a network function virtualization (NFV)-enabled and digital twin (DT)-assisted physical network for network service providers and network resource providers. It faces several key challenges like data privacy and different variation patterns of network traffic for multiple service function chain (SFC) requests. In view of this, we address the network traffic prediction problem by jointly considering the above key challenges in this manuscript. Specifically, we formulate the virtual network function (VNF) migration and SFC placement problems as integer linear programming (ILP) that aim to maximize acceptance revenues, minimize network resource costs, minimize energy consumption, and minimize migration cost. Then, we define the Markov Decision Process (MDP) for the network traffic prediction problem, and propose a model and algorithm to solve the problem. The simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve a better performance. | |
| 653 | |a Network function virtualization | ||
| 653 | |a Linear programming | ||
| 653 | |a Technological change | ||
| 653 | |a Collaboration | ||
| 653 | |a Deep learning | ||
| 653 | |a Integer programming | ||
| 653 | |a Mathematical models | ||
| 653 | |a Markov processes | ||
| 653 | |a Communications traffic | ||
| 653 | |a Optimization | ||
| 653 | |a Virtual networks | ||
| 653 | |a Privacy | ||
| 653 | |a Localization | ||
| 653 | |a Efficiency | ||
| 653 | |a Innovations | ||
| 653 | |a Scheduling | ||
| 653 | |a Energy costs | ||
| 653 | |a Digital twins | ||
| 653 | |a Neural networks | ||
| 653 | |a Manuscripts | ||
| 653 | |a Algorithms | ||
| 653 | |a Quality of service | ||
| 653 | |a Energy consumption | ||
| 653 | |a Predictions | ||
| 653 | |a Resource management | ||
| 700 | 1 | |a Liu, Ben | |
| 700 | 1 | |a Li, Jianyong | |
| 700 | 1 | |a Jia Linlin | |
| 773 | 0 | |t Electronics |g vol. 14, no. 20 (2025), p. 4129-4156 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3265899048/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3265899048/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3265899048/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |