CSR: Small: Heterogeneous Storage Systems with Emerging Technologies for Solving Big Data Problems

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The past decade has witnessed tremendous advances in computing, communication and storage technologies. With the unprecedented connectivity provided by Internet, many new data-driven applications have emerged and are being developed. The information technology (IT) infrastructure of this new era calls for low cost and high-performance storage, flexible ways of processing data for decision making and information retrieval, and keeping data for extremely long durations. This project intends to address certain aspects of these challenges. That is, 1) developing heterogeneous storage systems with low cost and high performance based on emerging storage technologies and 2) leveraging active storage devices for supporting decision making and information retrieval. An active storage device is a storage device having some additional, but limited processing power and can process data locally on the device. Active storage devices can be used to effectively filter out the unnecessary data from storage to memory to support decision making and information retrieval. This project will encourage innovative and creative thinking for integrated solutions that combine emerging storage technologies/devices to build storage systems supporting decision making and information retrieval, enhancing the fundamental understanding of large scale data management in big data era, and quickly developing prototypes to demonstrate the capabilities of these designs. This project is also closely collaborated with a number of industrial companies through NSF Industry-University Cooperative Research Center for Research in Intelligent Storage (CRIS). The outcomes of the project can be quickly adopted by storage industry. The expected project outcomes include fostering the advancement of science and technology especially for data-driven type of scientific research, making society more efficient with big data solutions/applications, and creating better ways of managing and migrating huge amount of available data with better performance and lower cost. Through collaboration with storage industry, the project will provide an ideal hands-on learning and development environment to teach both graduate and undergraduate students important system building and experimental skills that are critical for today's IT workforce. The project outcomes will be incorporated into classroom teaching of both course projects and the core courses in computer science and data science programs for both graduate and under-graduate students. The outcomes of the project include managing algorithms for new storage technologies/devices, new storage architectures, different storage systems designs, new data models, information access methods/algorithms and new ways to deliver information and making decisions. The research outcomes will be deposited and available to public in http://cris.cs.umn.edu.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.