Advancing segment routing technology: A new model for scalable and low-latency IP/MPLS backbone optimization
1 Independent Researcher, USA.
2 Independent Researcher, United Kingdom.
3 Independent Researcher, Canada.
4 CISCO, Nigeria.
5 Amstek Nigeria Limited.
Research Article
Open Access Research Journal of Science and Technology, 2022, 05(02), 077-095.
Article DOI: 10.53022/oarjst.2022.5.2.0056
Publication history:
Received on 28 May 2022; revised on 06 July 2022; accepted on 08 July 2022
Abstract:
Segment Routing (SR) is emerging as a transformative technology for optimizing IP/MPLS backbone networks, addressing critical challenges of scalability, latency, and operational efficiency. Unlike traditional network protocols, SR simplifies traffic engineering by encoding path information within packet headers, eliminating the need for intermediate state maintenance. This paper introduces a new model for leveraging Segment Routing to enhance the scalability and performance of IP/MPLS backbones, with a particular focus on low-latency routing and efficient resource utilization. The proposed model integrates Segment Routing over IPv6 (SRv6) and MPLS (SR-MPLS) to enable seamless interoperability across diverse network architectures. It incorporates machine learning algorithms for dynamic traffic prediction and adaptive path computation, optimizing end-to-end latency while maintaining high scalability. Furthermore, the model supports fine-grained service differentiation, allowing service providers to tailor quality of service (QoS) policies based on application requirements. A key innovation of this approach is the introduction of latency-aware segment identifiers (SIDs), which dynamically adapt to real-time network conditions. These latency-aware SIDs leverage telemetry data to minimize delay, making them particularly effective for latency-sensitive applications like video streaming, online gaming, and financial transactions. The model also addresses scalability challenges by reducing the control plane complexity, as Segment Routing does not require per-flow state maintenance in the core network. Comprehensive simulations and real-world deployments demonstrate the model’s effectiveness in reducing latency by up to 30% and improving network throughput by 25%, compared to traditional MPLS traffic engineering methods. Additionally, it significantly simplifies network operations, reducing configuration errors and operational overhead. The paper concludes with recommendations for the adoption of Segment Routing as a foundational technology for next-generation IP/MPLS backbones, emphasizing its potential to support emerging demands for ultra-low-latency, high-capacity, and agile network services.
Keywords:
Segment Routing (SR); IP/MPLS; Low Latency; Scalability; Srv6; SR-MPLS; Traffic Engineering; Network Optimization; Latency-Aware Sids; Machine Learning
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