PSMark: a distributed IoT benchmark for publish/subscribe under domain-based workloads
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Badolato, Christian, Nathan Samson, Houssam Hajj Hassan, Chih-Kai Huang, et al., “PSMark: A Distributed IoT Benchmark for Publish/Subscribe under Domain-Based Workloads,” paper presented at 24th IEEE International Conference on Pervasive Computing and Communications, Pisa, Italy, March 2026. https://inria.hal.science/hal-05517145/file/PerCom2026_PSMark.pdf
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Abstract
The Publish/Subscribe (pub/sub) paradigm is widely used in the Internet of Things (IoT). Standalone sensors, wearables, and other devices act as producers that publish messages to consumers such as edge servers or even other IoT devices. Selecting and configuring a pub/sub protocol for an IoT system requires considering network requirements, device reliability, and required Quality-of-Service guarantees. Pub/sub benchmarking suites can help compare expected behavior of various protocols, implementations, and network configurations. However, current pub/sub benchmarks focus primarily on stress testing systems assuming mostly static configurations of homogeneous publishers which are not representative of real-world IoT deployments. To address this, we present PSMark, a distributed, multi-protocol benchmark for evaluating topic-filtered pub/sub systems under workloads representative of real-world IoT environments. PSMark supports (i) workloads representative of heterogeneous IoT device deployments including variations in device communication parameters, (ii) evaluation of distributed IoT deployments with multiple data aggregation servers, (iii) cross-protocol measurements across MQTT and DDS, with extensibility to additional protocols, and (iv) a modular design for adding additional metrics and interfaces. We further construct twelve IoT-focused workloads derived from seven real-world datasets in the domains of manufacturing, healthcare, smart homes, and smart cities. Finally, we benchmark five popular MQTT brokers and one DDS implementation using PSMark and analyze their performance across multiple testbeds and Quality-of-Service settings.
