Radiator - efficient message propagation in context-aware systems

Applications such as Facebook, Twitter and Foursquare have brought the mass adoption of personal short messages, distributed in (soft) real-time on the Internet to a large number of users. These messages are complemented with rich contextual information such as the identity, time and location of the person sending the message (e.g., Foursquare has millions of users sharing their location on a regular basis, with almost 1 million updates per day). Such contextual messages raise serious concerns in terms of scalability and delivery delay; this results not only from their huge number but also because the set of user recipients changes for each message (as their interests continuously change), preventing the use of well-known solutions such as pub-sub and multicast trees. This leads to the use of non-scalable broadcast based solutions or point-to-point messaging. We propose Radiator, a middleware to assist application programmers implementing efficient context propagation mechanisms within their applications. Based on each user's current context, Radiator continuously adapts each message propagation path and delivery delay, making an efficient use of network bandwidth, arguably the biggest bottleneck in the deployment of large-scale context propagation systems. Our experimental results demonstrate a 20x reduction on consumed bandwidth without affecting the real-time usefulness of the propagated messages.