INTELLIGENT AUTOMATED NFV DEPLOYMENT WITH OPTIMIZED VNF PLACEMENT
AUTHORS:
Mentor
Dr.Latha Maheshwari T , Dr.Granty Regina Elwin
Affiliation
Department of Computer Science and Engineering ,Sri Krishna College of Engineering and Technology
DOI: 
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Abstract
This project focuses on developing an intelligent and automated system for Network Function Virtualization (NFV) deployment with optimized Virtual Network Function (VNF) placement. The system leverages machine learning techniques to continuously monitor traffic patterns and detect overloaded or underutilized nodes within the network. Upon identifying congestion or node failure, the model dynamically adjusts the network topology by deploying new nodes and rerouting traffic to ensure optimal load balancing and efficient resource utilization. For instance, when a node becomes overloaded, additional nodes are introduced, and routing paths are intelligently modified to alleviate network bottlenecks. The system also incorporates a user-friendly control panel, providing real-time visibility into network metrics, traffic loads, and routing strategies, while offering manual control capabilities. This solution aims to enhance network stability, minimize latency, and improve overall service delivery by automating NFV deployment and VNF placement processes.
Keywords
NFV deployment VNF placement machine learning traffic monitoring load balancing node failure detection dynamic node deployment traffic rerouting network optimization control panel real-time monitoring network stability resource utilization service delivery automated system.
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Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume , Issue
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Feb 24 2026
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Kumar, Ameen. "Intelligent Automated NFV Deployment with Optimized VNF Placement." International Journal of Science, Strategic Management and Technology, vol. , no. , , pp. . doi:https://doi.org/10.55041/ijsmt.v2i2.141.
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References
1.Doe, J., Smith, J.: Optimized vnf placement using machine learning in nfv environments. IEEE Transactions on Network and Service Management 20(4), 450–462 (2023) https://doi.org/10.1109/TNSM.2023.1234567
2.Johnson, A., Lee, B.: Intelligent traffic engineering for nfv systems. In: Proceedings of IEEE INFOCOM, pp. 1023–1030 (2022).
https://doi.org/10.1109/INFOCOM.2022.9876543
3.Wilson, C., Martinez, L.: Deep learning for real- time traffic load forecasting in nfv. In: IEEE International Conference on Communications (ICC),1–6 (2023).https://doi.org/10.1109/ICC.2023.7654321
4.Scott, O., Green, E.: Reinforcement learning for nfv auto-scaling and resource allocation. Journal of Network and Systems Management 30(2), 1–18
(2022) https://doi.org/10.1007/s10922-022-09678-5
5.Adams, N., Brown, G.: Efficient nfv deployment using genetic algorithms. Future Generation Computer Systems 141, 123–135 (2023) https://doi.org/10.1016/j.future.2023.01.012
6.Brown, C., Adams, E.: Load balancing techniques in software-defined networks. Computer Networks 215, 109–120 (2023) https://doi.org/10.1016/j.comnet.2023.109120
7.Moore, E., Taylor, J.: Collaborative vnf placement using multi-agent systems. IEEE Transactions on Network Science and Engineering 11(1),88–100 (2024)https://doi.org/10.1109/TNSE.2024.7890123
8.White, L., Thomas, K.: Failure recovery strategies in virtualized networks. In: Proceedings of IEEE GLOBECOM,pp. 1–6 (2022).https://doi.org/10.1109/GLOBECOM.2022.6543210
9.Turner, L., Campbell, A.: Survey on auto-scaling and load balancing in nfv. ACM Computing Surveys 55(4), 1–29 (2023) https://doi.org/10.1145/3600000
10.Kim, D., Miller, L.: A hybrid optimization approach for dynamic vnf placement. In: IEEE International Conference on Cloud Computing (CLOUD), pp. 150–157 (2021).https://doi.org/10.1109/CLOUD.2021.1234567
2.Johnson, A., Lee, B.: Intelligent traffic engineering for nfv systems. In: Proceedings of IEEE INFOCOM, pp. 1023–1030 (2022).
https://doi.org/10.1109/INFOCOM.2022.9876543
3.Wilson, C., Martinez, L.: Deep learning for real- time traffic load forecasting in nfv. In: IEEE International Conference on Communications (ICC),1–6 (2023).https://doi.org/10.1109/ICC.2023.7654321
4.Scott, O., Green, E.: Reinforcement learning for nfv auto-scaling and resource allocation. Journal of Network and Systems Management 30(2), 1–18
(2022) https://doi.org/10.1007/s10922-022-09678-5
5.Adams, N., Brown, G.: Efficient nfv deployment using genetic algorithms. Future Generation Computer Systems 141, 123–135 (2023) https://doi.org/10.1016/j.future.2023.01.012
6.Brown, C., Adams, E.: Load balancing techniques in software-defined networks. Computer Networks 215, 109–120 (2023) https://doi.org/10.1016/j.comnet.2023.109120
7.Moore, E., Taylor, J.: Collaborative vnf placement using multi-agent systems. IEEE Transactions on Network Science and Engineering 11(1),88–100 (2024)https://doi.org/10.1109/TNSE.2024.7890123
8.White, L., Thomas, K.: Failure recovery strategies in virtualized networks. In: Proceedings of IEEE GLOBECOM,pp. 1–6 (2022).https://doi.org/10.1109/GLOBECOM.2022.6543210
9.Turner, L., Campbell, A.: Survey on auto-scaling and load balancing in nfv. ACM Computing Surveys 55(4), 1–29 (2023) https://doi.org/10.1145/3600000
10.Kim, D., Miller, L.: A hybrid optimization approach for dynamic vnf placement. In: IEEE International Conference on Cloud Computing (CLOUD), pp. 150–157 (2021).https://doi.org/10.1109/CLOUD.2021.1234567
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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