Systematic Evaluation of Three Data Marshalling Approaches for Distributed Software Systems
Paper i proceeding, 2015

Cyber-physical systems like robots and self-driving vehicles comprise complex software systems. Their software is typically realized as distributed agents that are responsible for dedicated tasks like sensor data handling, sensor data fusion, or action planning. The modular design allows a flexible deployment as well as algorithm encapsulation to exchange software modules where needed. Such distributed software exchanges data using a data marshalling layer to serialize and deserialize data structures between a sending and receiving entity. In this article, we are systematically evaluating Google Protobuf, LCM, and our self-adaptive delta marshalling approach by using a generic description language, of which instances can be composed at runtime. Our results show that Google Protobuf performs well for small messages composed mainly by integral field types; the self-adaptive data marshalling approach is efficient if four or more fields of type double are present, and LCM outperforms both when a mix of many integral and double fields is used.

Distributed software systems

Data marshalling

Self-adaptive data marshalling

Författare

Hugo Sica de Andrade

Chalmers, Data- och informationsteknik, Software Engineering

Federico Giaimo

Chalmers, Data- och informationsteknik, Software Engineering

Christian Berger

Göteborgs universitet

Ivica Crnkovic

Chalmers, Data- och informationsteknik, Software Engineering

Proceedings of the 15th Workshop on Domain-Specific Modelling

71-76

Ämneskategorier

Programvaruteknik

DOI

10.1145/2846696.2846705

ISBN

978-1-4503-3903-2