Latency-Aware and Energy-Efficient Switching Protocols for Next-Generation IP Backbone Networks Using AI-Augmented Control Planes
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P106Keywords:
Latency-aware switching, energy efficiency, IP backbone networks, AI-augmented control plane, machine learning, reinforcement learning, QoS, SDN, routing optimizationAbstract
The rapid expansion of data-intensive applications, cloud services, IoT ecosystems, and real-time communication platforms has drastically increased the demands on IP backbone networks. These networks must deliver ultra-low latency and optimized energy consumption while maintaining high throughput and reliability. Traditional IP switching protocols and static control plane architectures are ill-equipped to meet these demands due to their rigidity and lack of adaptability. This paper presents a comprehensive exploration and novel implementation of latency-aware and energy-efficient switching protocols enabled by AI-augmented control planes for next-generation IP backbone networks. The proposed framework leverages machine learning (ML) and deep reinforcement learning (DRL) models to dynamically monitor, predict, and adapt network flows based on latency constraints and energy profiles. Through continuous learning from traffic behavior, topology changes, and performance metrics, the AI-augmented control plane can make informed decisions that optimize both quality of service (QoS) and energy efficiency (EE). A modular architecture is designed, consisting of three core components: (i) a Latency Prediction Module (LPM) trained on historical traffic and delay patterns, (ii) an Energy Consumption Optimizer (ECO) based on multi-objective optimization, and (iii) a Reinforcement Learning Policy Engine (RLPE) for adaptive switching decisions.
The synergy between these modules allows for proactive switching and routing tailored to real-time network conditions. Simulation and test bed evaluations of emulated Tier-1 ISP topologies demonstrate significant improvements, including an average latency reduction of 35%, energy savings of 27%, and improved throughput stability under fluctuating traffic. The system dynamically bypasses congestion, powers down idle links, and reroutes delay-sensitive data through low-latency paths. Comparative analyses with OSPF, IS-IS, and SDN-based approaches establish the superiority of the AI-augmented protocols in diverse traffic scenarios. The methodology is validated using datasets from CAIDA and real-world BGP traffic traces. Evaluation metrics include latency deviation, link utilization, packet loss, and energy-delay product (EDP). Key findings reveal the potential of AI-driven intelligence to revolutionize backbone network control by enhancing responsiveness, sustainability, and service quality. This paper contributes a novel AI-based protocol framework, an implementation-ready control plane design, and extensive quantitative evaluations that pave the way for practical deployment in ISP environments. Our findings underscore the importance of adaptive intelligence in addressing the dual challenges of latency and energy in future IP backbone architectures
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