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Systematic Evaluation of Three Data Marshalling Approaches for Distributed Software Systems

Hugo Sica de Andrade (Institutionen för data- och informationsteknik, Software Engineering (Chalmers)) ; Federico Giaimo (Institutionen för data- och informationsteknik, Software Engineering (Chalmers)) ; Christian Berger ; Ivica Crnkovic (Institutionen för data- och informationsteknik, Software Engineering (Chalmers))
Proceedings of the 15th Workshop on Domain-Specific Modelling p. 71-76. (2015)
[Konferensbidrag, refereegranskat]

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.

Nyckelord: Data marshalling, Distributed software systems, Self-adaptive data marshalling



Denna post skapades 2016-01-04. Senast ändrad 2016-07-07.
CPL Pubid: 229542

 

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Institutioner (Chalmers)

Institutionen för data- och informationsteknik, Software Engineering (Chalmers)
Institutionen för data- och informationsteknik (GU) (GU)

Ämnesområden

Programvaruteknik

Chalmers infrastruktur