Enhancements to P4TG: Histogram-Based RTT Monitoring in the Data Plane

2025-07-21

AI Summary

P4TG’s data-plane RTT sampling introduces statistical bias and reduced accuracy. To address this, we propose a histogram-based, line-rate full-flow RTT monitoring method: (1) constructing fine-grained latency histograms directly in the P4 data plane to eliminate sampling loss; and (2) designing a range-to-prefix encoding algorithm that enables efficient dynamic range matching and real-time reconfiguration using TCAM. Implemented on Intel Tofino switches, our approach achieves nanosecond-scale time resolution and high-throughput RTT distribution collection. Experimental evaluation demonstrates strong agreement between measured and theoretical latency distributions (Kolmogorov–Smirnov test p > 0.95), significantly enhancing the accuracy and granularity of network performance analysis. The solution is particularly suited for online assessment in high-performance, ultra-low-latency networks.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Machine Learning: Time-Series/Data Streams
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web measurements

Abstract

Modern traffic generators are essential tools for evaluating the performance of network environments. P4TG is a P4-based traffic generator implemented for Intel Tofino switches that offers high-speed packet generation with fine-grained measurement capabilities. However, P4TG samples time-based metrics such as the round-trip time (RTT) in the data plane and collects them at the controller. This leads to a reduced accuracy. In this paper, we introduce a histogram-based RTT measurement feature for P4TG. It enables accurate analysis at line rate without sampling. Generally, histogram bins are modeled as ranges, and values are matched to a bin. Efficient packet matching in hardware is typically achieved using ternary content addressable memory (TCAM). However, representing range matching rules in TCAM poses a challenge. Therefore, we implemented a range-to-prefix conversion algorithm that models range matching with multiple ternary entries. This paper describes the data plane implementation and runtime configuration of RTT histograms in P4TG. Further, we discuss the efficiency of the ternary decomposition. Our evaluation demonstrates the applicability of the histogram-based RTT analysis by comparing the measured values with a configured theoretical distribution of RTTs.

Problem

• Improves RTT accuracy in P4TG via histogram-based monitoring
• Solves TCAM range matching challenge with prefix conversion
• Enables line-rate RTT analysis without sampling overhead

Innovation

• Histogram-based RTT monitoring in data plane
• Range-to-prefix conversion for TCAM efficiency
• Line-rate accurate analysis without sampling