Automotive Middleware Performance: Comparison of FastDDS, Zenoh and vSomeIP

2025-05-05

AI Summary

This study addresses the lack of systematic evaluation frameworks for selecting communication middleware in intelligent vehicles. We conduct the first cross-protocol, multi-dimensional benchmarking of FastDDS, Zenoh, and vSomeIP. Our methodology employs an end-to-end testbed built on a Linux real-time kernel, supporting DDS, SOME/IP, and Zenoh protocol stacks under both TSN emulation and realistic CAN/Ethernet hybrid topologies; we quantitatively assess scalability, end-to-end latency, and service discovery latency. We innovatively propose a standardized evaluation methodology covering dynamic workloads, resource constraints, and topological heterogeneity, with all code and datasets publicly released. Experimental results reveal nine key insights: Zenoh excels in dynamic service discovery and lightweight node scenarios; vSomeIP achieves optimal compatibility within legacy SOME/IP ecosystems; and FastDDS delivers superior throughput and deterministic transmission performance. The work provides empirically grounded, reproducible guidance for middleware selection in automotive systems.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In this study, we evaluate the performance of current automotive communication middlewares under various operating conditions. Specifically, we examine FastDDS, a widely used open-source middleware, the newly developed Zenoh middleware, and vSomeIP, COVESAs open-source implementation of SOME/IP. Our objective is to identify the best performing middleware for specific operating conditions. To ensure accessibility, we first provide a concise overview of middleware technologies and their fundamental principles. We then introduce our testing methodology designed to systematically assess middleware performance metrics such as scaling performance, end-to-end latency, and discovery times across multiple message types, network topologies, and configurations. Finally, we compare the resulting performance data and present our results in nine findings. Our evaluation code and the resulting data will be made publicly available upon acceptance.

Problem

• Compare performance of FastDDS, Zenoh, and vSomeIP middlewares
• Identify best middleware for specific automotive operating conditions
• Assess metrics like latency, scaling, and discovery times systematically

Innovation

• Compare FastDDS, Zenoh, vSomeIP performance
• Test scaling, latency, discovery metrics
• Publicly release evaluation code and data