Hu, Y.; Liu, B.; Li, J.; Zhu, L.; Han, J.; Cai, Z.; Zhang, J. Network Traffic Prediction in Edge-Cloud Continuum Network for Multiple Network Service Providers. Preprints2024, 2024071484. https://doi.org/10.20944/preprints202407.1484.v1
APA Style
Hu, Y., Liu, B., Li, J., Zhu, L., Han, J., Cai, Z., & Zhang, J. (2024). Network Traffic Prediction in Edge-Cloud Continuum Network for Multiple Network Service Providers. Preprints. https://doi.org/10.20944/preprints202407.1484.v1
Chicago/Turabian Style
Hu, Y., Zengyu Cai and Jie Zhang. 2024 "Network Traffic Prediction in Edge-Cloud Continuum Network for Multiple Network Service Providers" Preprints. https://doi.org/10.20944/preprints202407.1484.v1
Abstract
Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtualized Network Functions, adapting services to the complex and real-time network environment for enhanced network performance. Despite the benefits of NFV, virtual network functions (VNFs) migration and the energy consumption pose significant challenges due to the dynamic nature of the physical network, especially in the edge-cloud continuum network. Currently, people tackle this challenge by predicting the network traffic and then proactively migrating the VNFs using the predicted values. There are two challenges here. First, when SFCs belong to different network service providers, the historical network traffic data is held by the service providers, and they are reluctant to share the historical data due to privacy concerns. Without the historical network traffic data of each SFC, the resource provider that owns the underlying network cannot effectively predict the network traffic; in addition, without the feedback of the migration results of the underlying physical network, the network traffic prediction will be difficult to match the needs of the physical network. In this manuscript, we solve the problem of network traffic prediction in the NFV-based edge-cloud continuum network for multiple network service providers. We aim to improve migration performance and reduce energy consumption while ensuring resource and latency requirements. To protect the privacy of each network service provider, we apply an FL algorithm. After that, to improve the migration performance, we tailor the loss function. In the loss function, factors such as the number of migrations, the number of migration failures, and the energy consumption of the NFV-based edge-cloud continuum network are added. Additionally, to obtain the results of the SFC migration, we develop a mathematical model for the problem of multiple network service providers in the NFV-based network. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption while improving the SFC acceptance ratio compared to state-of-the-art schemes which only use the difference between predicted and actual flows to define the loss function.
Keywords
Edge-Cloud Continuum; Network Function Virtualization; Virtual Network Function Migration; Federated Learning
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
