UC Riverside

The predominantly content-oriented use of today's networks has led to the development of information-Centric Networking (ICN) paradigms and network architectural designs. At the core of such designs is naming, which organizes and guides the delivery and dissemination of information in the network. This dissertation studies the correctness of such name-based networking, improving it for better scalability, and enhancing its functionality in real-world applications.

As a first topic, we propose Name Space Analysis (NSA), a network verification framework to model and analyze name-based data planes of Named Data Networks. NSA supports primitives fundamentally different from those of traditional host-centric IP network verification, such as checking host-to-content reachability in addition to the traditional host-to-host reachability considerations. We also design and formally analyze name-based delivery for inter-operation of ICNs with existing IP networks.

State-of-the-art ICN designs rely on strictly-hierarchical naming frameworks and pull-based request/response models, which can be inadequate when it comes to complex information structures and many-senders-to-many-receivers delivery. We propose POISE, a name-based and recipient-based publish/subscribe architecture for efficient information dissemination that supports complex graph-based namespaces with a workload-driven namespace graph partitioning for multicast core migration. We demonstrate that POISE achieves better efficiency in timely delivery and elimination of traffic concentration than existing alternatives.

Next, we consider infrastructure-less intermittently-connected network environments, where consistency of replicated databases is especially challenging. We propose CoNICE, a framework to ensure consistent dissemination of updates in such environments. Our proposed name-based coordinator-less consensus method improves completeness and the convergence latency in ordering updates, all with lower communication overhead. We study its correctness and scalability and also demonstrate its performance benefits.

We then look at how names can be learned from the content itself. We propose a framework that integrates Natural Language Processing techniques with Information-Centric dissemination, considering the role of social media-based incident reporting in disaster response. The framework introduces a social media engine to intelligently map social media posts to the right names, to steer posts (content) to the most relevant first responders in a timely manner. We further show how these social media engines can be enhanced, using active and federated learning, for better accuracy.