CNS Core: Small: New Caching Paradigms for Distributed and Dynamic Networks

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Over the last few decades, Content Distribution Networks (CDNs) have been used to cache data of popular interest closer to the end-users in order to make them rapidly accessible whenever requested. This results in substantial time savings from an end-user’s perspective when retrieving popular content like video feeds. It also helps to reduce the bandwidth demands on the overall network. This is achieved by developing efficient mechanisms that employ popularity-based indicators to store or evict content from the cache memory. However, with the advent of emerging technologies such as the Internet-of-Things (IoT) and cyberphysical systems, the dynamics and requirements of networks are witnessing significant changes that call for fundamentally new caching solutions that extend beyond the purview of CDNs to the wireless edge and the end-user devices. These new networks will be composed of a large number of highly mobile devices (e.g., mobile phones) with intermittent, delay-sensitive, and personalized demand to be supported over resource-limited wireless channels. To address these new challenges, this project undertakes the task of democratizing caching by proposing a systematic methodology that encompasses the wireless edge and the end-users in the design of provably good caching strategies for increasingly dynamic and disparate networks. This project is motivated by the observation that caching within and at the endpoints of networks must differ fundamentally from each other, as well as from existing caching strategies optimized for today's CDNs. In particular, the project identifies and systematically investigates complementary scenarios that must employ different caching principles based on the position of the memory space and the nature of demand it receives. In each scenario, the project takes a holistic approach to the design to incorporate generation and demand dynamics, wireless communication capabilities and limitations, variety of uncertainties in the traffic and environment, and quality-of-service requirements. This research is multi-disciplinary and will bring together elements from queueing, scheduling, anomaly detection, coding, learning and control theories towards the design of integrated and adaptive caching solutions. This interdisciplinary research will be integrated into the curricula and will help train the next generation of engineers and academicians with the tools it takes to succeed in solving increasingly complex and challenging problems. The investigators are committed to recruit women and under-represented minority students in their research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.