Achieving Ultra-Low Latency in Network Function Virtualization through Intelligent Service Function Chaining

Abstract

This study presents an experimental evaluation of latency optimization techniques in Network Function Virtualization (NFV) using intelligent Service Function Chaining (SFC). Results show that edge-based VNF deployment reduces latency by up to 40% compared to centralized setups. Segment routing further lowers latency by 20% by minimizing control plane overhead. Programmable data planes, leveraging P4-capable switches, improve packet processing efficiency by 10-15%. Intelligent SFC consistently outperforms traditional approaches, exhibiting smoother latency growth across increasing service chain lengths. A machine learning-based orchestration system demonstrated predictive reconfiguration capabilities, maintaining latency thresholds under high-traffic conditions. Comparative analysis reveals that combining edge deployment, segment routing, programmable data planes, and intelligent orchestration yields the best performance in terms of latency, throughput, and CPU utilization. However, challenges persist, including hardware dependencies, model training requirements, and orchestration scalability. Results validate the advantages of intelligent SFC in latency reduction, throughput enhancement, and resource efficiency, confirming its suitability for ultra-low latency NFV environments.