CRI: II-NEW: A Big Data Professing Infrastructure for Smart Energy Systems

CRI：II-NEW：智能能源系统的大数据专业基础设施

• 批准号: 1730488
• 负责人: Xian-He Sun
• 金额: $ 40万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1730488&HistoricalAwards=false
• 关键词: CRI; II; NEW; Big; Data

Illinois Institute of Technology (IIT) has built the first-ever fully-functional campus microgrid on its Chicago Main Campus. Massive and rich data produced by this unique live laboratory are invaluable assets for the research community to enable further innovation in critical energy systems. However, data science in the energy systems faces demanding challenges, such as inadequate data processing speed, heterogeneous and error-prone data source, and strong real-time performance requirement. To enable research to address those challenges, this project will build a research infrastructure IDES (Integrated Data analytics for Energy Systems) to integrate high-performance computing power, in terms of both hardware and software, with the existing campus microgrid facility to meet the requirements of big data processing and analytics in energy systems.IDES will be the first big data research infrastructure that integrates with a functioning smart microgrid. IDES consists of network devices, an integrated computer/file server cluster for high-performance data processing and storage, as well as a software-defined networking-based microgrid communication cyber-infrastructure. The project will enable the IIT microgrid to serve as a living laboratory, not only to advance power grid technologies but also to advance the big data analytics and cyber-security technologies. The project will also establish a partnership among power system researchers, computer scientists, electric utilities, IT support organizations, and other industrial partners to build a high-performance infrastructure for big data analytics in energy systems. In addition, the project will train students with the much-needed skills in high-performance data processing, microgrid, networking and cyber-security. The team will disseminate the research outcomes of this project to the research community, students, educators, industry professionals and the public through collaboration with the DoE-funded Smart Grid Education and Workforce Training Center at IIT.

伊利诺伊理工学院（IIT）在其芝加哥主校区建立了有史以来第一个全功能的校园微电网。这个独特的实时实验室产生的海量和丰富的数据是研究界的宝贵资产，有助于关键能源系统的进一步创新。然而，能源系统中的数据科学面临着苛刻的挑战，例如数据处理速度不足，异构且容易出错的数据源以及强烈的实时性能要求。为了使研究能够应对这些挑战，该项目将建立一个研究基础设施IDES（能源系统集成数据分析），在硬件和软件方面集成高性能计算能力，与现有的校园微电网设施，以满足大数据处理和分析的能源系统的要求。IDES将是第一个大数据研究基础设施，集成了一个功能智能微电网IDES由网络设备、用于高性能数据处理和存储的集成计算机/文件服务器集群以及基于软件定义网络的微电网通信网络基础设施组成。该项目将使IIT微电网成为一个活的实验室，不仅可以推进电网技术，还可以推进大数据分析和网络安全技术。该项目还将在电力系统研究人员、计算机科学家、电力公司、IT支持组织和其他工业合作伙伴之间建立伙伴关系，为能源系统中的大数据分析建立高性能基础设施。此外，该项目还将培训学生掌握高性能数据处理、微电网、网络和网络安全方面急需的技能。该团队将通过与能源部资助的IIT智能电网教育和劳动力培训中心合作，向研究界，学生，教育工作者，行业专业人士和公众传播该项目的研究成果。

项目成果

A hardware-in-the-loop emulation testbed for high fidelity and reproducible network experiments

用于高保真和可重现网络实验的硬件在环仿真测试平台

• DOI: 10.1109/wsc.2017.8247803
• 发表时间: 2017
• 期刊: 2017 Winter Simulation Conference (WSC
• 作者: Wu, Xiaoliang;Yang, Qi;Liu, Xin;Jin, Dong;Lee, Cheol Won
• 通讯作者: Lee, Cheol Won

A Distributed Virtual Time System on Embedded Linux for Evaluating Cyber-Physical Systems

嵌入式 Linux 上用于评估信息物理系统的分布式虚拟时间系统

• DOI: 10.1145/3316480.3322895
• 发表时间: 2019
• 期刊: ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
• 作者: Hannon, Christopher;Yan, Jiaqi;Liu, Yuan-An;Jin, Dong
• 通讯作者: Jin, Dong

I/O Acceleration via Multi-Tiered Data Buffering and Prefetching

通过多层数据缓冲和预取实现 I/O 加速

• DOI: 10.1007/s11390-020-9781-1
• 发表时间: 2020
• 期刊: Journal of Computer Science and Technology
• 影响因子: 1.9
• 作者: Kougkas, Anthony;Devarajan, Hariharan;Sun, Xian-He
• 通讯作者: Sun, Xian-He

Distributed Virtual Time-Based Synchronization for Simulation of Cyber-Physical Systems

用于信息物理系统仿真的分布式虚拟基于时间的同步

• DOI: 10.1145/3446237
• 发表时间: 2021
• 期刊: ACM Transactions on Modeling and Computer Simulation
• 影响因子: 0.9
• 作者: Hannon, Christopher;Yan, Jiaqi;Jin, Dong
• 通讯作者: Jin, Dong

HCL: Distributing Parallel Data Structures in Extreme Scales

• DOI: 10.1109/cluster49012.2020.00035
• 发表时间: 2020-09
• 期刊: 2020 IEEE International Conference on Cluster Computing (CLUSTER)
• 作者: H. Devarajan;Anthony Kougkas;Keith Bateman;Xian-He Sun