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

过去十年见证了计算、通信和存储技术的巨大进步。随着互联网提供前所未有的连通性，许多新的数据驱动的应用程序已经出现并正在开发中。新时代的信息技术(IT)基础设施要求低成本和高性能的存储，灵活的数据处理方式以供决策和信息检索，并将数据保存极长的时间。该项目旨在解决这些挑战的某些方面。也就是说，1)基于新兴的存储技术开发低成本、高性能的异构存储系统；2)利用主动存储设备支持决策和信息检索。活动存储设备是具有一些附加但有限的处理能力的存储设备，并且可以在设备上本地处理数据。主动存储设备可以有效地从存储到内存中过滤掉不必要的数据，以支持决策和信息检索。该项目将鼓励对集成解决方案的创新和创造性思维，这些解决方案将结合新兴的存储技术/设备来构建支持决策和信息检索的存储系统，增强对大数据时代大规模数据管理的基本理解，并快速开发原型以展示这些设计的能力。该项目还通过NSF智能存储研究产学合作研究中心(CRIS)与多家工业公司密切合作。项目成果可迅速被仓储行业采用。预期的项目成果包括促进科学技术的进步，特别是数据驱动型科学研究的进步，通过大数据解决方案/应用程序提高社会效率，以及创建以更好的性能和更低的成本管理和迁移大量可用数据的更好方法。通过与存储行业的合作，该项目将提供一个理想的实践学习和开发环境，向研究生和本科生传授对当今IT员工至关重要的重要系统建设和实验技能。项目成果将纳入课程项目的课堂教学，以及研究生和本科生的计算机科学和数据科学课程的核心课程。该项目的成果包括为新的存储技术/设备、新的存储体系结构、不同的存储系统设计、新的数据模型、信息访问方法/算法以及提供信息和决策的新方式管理算法。研究成果将保存在http://cris.cs.umn.edu.This奖项中并向公众提供，该奖项反映了美国国家科学基金会的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.