Scaling and Load Testing Location-based Publish and Subscribe
Bertil Chapuis and BenoƮt Garbinato
University of Lausanne, University of Lausanne

The rise of the Internet of things (IoT) poses massive scalability issues for location-based services. More particularly, location-aware publish and subscribe services are struggling to scale out the computation of matches between publications and subscriptions that continuously update their location. In this demonstration paper, we propose a novel distributed and horizontally scalable architecture for location-aware publish and subscribe. Our middleware architecture relies on a multistep routing mechanism based on consistent hashing and range partitioning. To demonstrate its scalability, we present a traffic data generator, which, in contrast to existing generators, can be used to perform real-time load tests. Finally, we show that our architecture can be deployed on a small 10-node cluster and can process up to 80,000 location updates per second producing 25,000 matches per seconds.