Offering fluid multi-device interactions to users while protecting their privacy largely remains an ongoing challenge. Existing approaches typically use a peer-to-peer design and flood session information over the network, resulting in costly and often unpractical solutions. In this paper, we propose SPRINKLER, a decentralized probabilistic dissemination protocol that uses a gossip-based learning algorithm to intelligently propagate session information to devices a user is most likely to use next. Our solution allows designers to efficiently trade off network costs for fluidity, and is for instance able to reduce network costs by up to 80% against a flooding strategy while maintaining a fluid user experience.

Modern large scale distributed systems increasingly espouse sophisticated distributed architectures characterized by complex distributed structural invariants. Unfortunately, maintaining these structural invariants at scale is time consuming and error prone, as developers must take into account asynchronous failures, loosely coordinated subsystems and network delays. To address this problem, we propose PLEIADES, a new framework to construct and enforce large-scale distributed structural invariants under aggressive conditions. PLEIADES combines the resilience of self-organizing overlays, with the expressiveness of an assembly-based design strategy. The result is a highly survivable framework that is able to dynamically maintain arbitrary complex distributed structures under aggressive crash failures. Our evaluation shows in particular that PLEIADES is able to restore the overall structure of a 25,600 node system in less than 11 asynchronous rounds after half of the nodes have crashed.

Allowing users to navigate seamlessly between their personal devices while protecting their privacy remains today an ongoing challenge. Existing solutions rely on peer-to-peer designs, and blindly flood the network with session messages. It is particularly hard to come up with proposals that are both costefficient and dependable while relying on poorly connected mobile appliances. We propose CASCADE, a distributed protocol to share applicative sessions among one's devices. Our proactive session handoff algorithm takes inspiration from the BitTorrent P2P file sharing protocol, but adapts it to the specific characteristics of our problem. It eschews in particular trackers, and limits the seeders of each session to the devices most likely to be used next, as computed by a decentralized aggregation protocol. A key aspect of our approach is to trade off network costs for reliability, while providing a faster session handoff than centralized solutions in the vast majority of the cases.

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