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Bounding the Data-Delivery Latency of DDS Messages in Real-Time Applications

Authors Gerlando Sciangula, Daniel Casini, Alessandro Biondi, Claudio Scordino, Marco Di Natale

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Gerlando Sciangula
  • TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy
  • Huawei Research Center, Pisa, Italy
Daniel Casini
  • TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy
  • Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
Alessandro Biondi
  • TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy
  • Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
Claudio Scordino
  • Huawei Research Center, Pisa, Italy
Marco Di Natale
  • TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy
  • Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy

Funding

This work has been supported by EIT Urban Mobility, an initiative of the European Institute of Innovation and Technology (EIT), a body of the European Union. The work has also been partially supported by the project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU and the European Union’s Horizon Europe Framework Programme project NANCY under the grant agreement No. 101096456.

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Gerlando Sciangula, Daniel Casini, Alessandro Biondi, Claudio Scordino, and Marco Di Natale. Bounding the Data-Delivery Latency of DDS Messages in Real-Time Applications. In 35th Euromicro Conference on Real-Time Systems (ECRTS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 262, pp. 9:1-9:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ECRTS.2023.9

Abstract

Many modern applications need to run on massively interconnected sets of heterogeneous nodes, ranging from IoT devices to edge nodes up to the Cloud. In this scenario, communication is often implemented using the publish-subscribe paradigm. The Data Distribution Service (DDS) is a popular middleware specification adopting such a paradigm. The DDS is becoming a key enabler for massively distributed real-time applications, with popular frameworks such as ROS 2 and AUTOSAR Adaptive building on it. However, no formal modeling and analysis of the timing properties of DDS has been provided to date. This paper fills this gap by providing an abstract model for DDS systems that can be generalized to any implementation compliant with the specification. A concrete instance of the generic DDS model is provided for the case of eProsima’s FastDDS, which is eventually used to provide a real-time analysis that bounds the data-delivery latency of DDS messages. Finally, this paper reports on an evaluation based on a representative automotive application from the WATERS 2019 challenge by Bosch.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Real-time schedulability
Keywords
  • DDS
  • real-time systems
  • response-time analysis
  • end-to-end latency
  • CPA

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