Towards Sub-millisecond Latency and Guaranteed Bit Rates in 5G User Plane

2025-10-31

AI Summary

To address the stringent requirements of 5G and beyond—namely per-flow bandwidth guarantees, microsecond-scale end-to-end latency, and dynamic QoS provisioning—this work tackles the fundamental limitation of traditional fixed-function networks, which struggle to support diverse 3GPP QoS configurations in cloud-native architectures. We propose the first fully programmable data plane model for transport networks that comprehensively supports all 3GPP-defined QoS resource types. Implemented in P4 on Intel Tofino switches, our design enables flow-level fine-grained classification, per-flow rate limiting, strict priority scheduling, and latency-aware queue management. Experimental evaluation demonstrates sub-1 ms end-to-end latency for critical flows, near-zero packet loss, and robust QoS stability under congestion. The solution significantly enhances service assurance capabilities for ultra-reliable low-latency applications.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Machine Learning: Hardware-aware ML
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Next-generation services demand stringent Quality of Service (QoS) guarantees, such as per-flow bandwidth assurance, ultra-low latency, and traffic prioritization, posing significant challenges to 5G and beyond networks. As 5G network functions increasingly migrate to edge and central clouds, the transport layer becomes a critical enabler of end-to-end QoS compliance. However, traditional fixed-function infrastructure lacks the flexibility to support the diverse and dynamic QoS profiles standardized by 3GPP. This paper presents a QoS-aware data plane model for programmable transport networks, designed to provide predictable behavior and fine-grained service differentiation. The model supports all 3GPP QoS resource types and integrates per-flow metering, classification, strict priority scheduling, and delay-aware queuing. Implemented on off-the-shelf programmable hardware using P4 and evaluated on an Intel Tofino switch, our approach ensures per-flow bandwidth guarantees, sub-millisecond delay for delay-critical traffic, and resilience under congestion. Experimental results demonstrate that the model achieves microsecond-level latencies and near-zero packet loss for mission-critical flows, validating its suitability for future QoS-sensitive applications in 5G and beyond.

Problem

• Achieving sub-millisecond latency in 5G networks
• Guaranteeing per-flow bandwidth and QoS requirements
• Supporting diverse 3GPP QoS profiles dynamically

Innovation

• Programmable transport networks with QoS-aware data plane
• Per-flow metering, classification, and strict priority scheduling
• Off-the-shelf hardware implementation achieving microsecond-level latencies