Scalable DDS Discovery Protocols Based on Bloom Filters

Áreas de investigación:

• Methodology - Machine learning

Año:

2007

Tipo de publicación:

Artículo en conferencia

Autores:

• Sánchez-Monedero, Javier
• Lopez-Soler, J. M.
• Povedano-Molina, J.

Título del libro:

Real-time and Embedded Systems Workshop, Arlington VA, USA

Abstract:

The OMG Data Distribution Service (DDS) standard has been recently adopted for real-time data communications. In any DDS platform, to deal with heterogeneous environments and to provide transparent communications, an entity discovery protocol is required. Broadly speaking, an exhaustive discovery approach could result in quadratic complexity. Obviously, the scalability of DDS infrastructure is somehow limited by the adopted discovery protocol. In this work, inspired by peer-to-peer and agent based networking services, we propose a preliminary study on scalable discovery protocols for DDS. In particular, we propose and evaluate solutions to the discovery DDS scalability problem by using Bloom Filters (BF).