CSR: Small: Enhancing Timeliness and Power-Efficiency of Real-Time Data Services

CSR：小：提高实时数据服务的及时性和能效

• 批准号: 2326796
• 负责人: Kyoung-Don Kang
• 金额: $ 59.91万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326796&HistoricalAwards=false
• 关键词: CSR; Small; Enhancing; Timeliness; Power

Real-time data services play a crucial role in supporting data-intensive real-time applications, such as smart transportation, healthcare, and manufacturing, with significant societal value. These applications require cost-efficient real-time embedded databases capable of processing real-time data service requests in a timely fashion, using fresh data that represent the current real-world status, while also conserving power. This project aims to investigate novel methodologies to promote the progress of science in this essential yet underexplored area of research. Furthermore, by improving resource and power efficiency, it aims to considerably alleviate the challenges of deploying real-time data services in deeply embedded systems that have limited resources. The broader impacts of the project include training undergraduates and graduates, including underrepresented groups of students, outreach activities, and open-source software.The goal of this research project is to bridge the knowledge gap on power-efficient real-time data services by investigating data-centric approaches that can improve both timeliness and power efficiency without compromising either. Achieving this objective, however, is challenging due to several factors, including varying arrival rates and data needs of user transactions based on the current real-world status, data and resource contention, and stringent timing, resource, and power constraints in embedded systems. To tackle such challenges, the research will (1) investigate a new effective real-time transaction model and data-centric approaches to reducing workloads that identify actual data requirements and freshness needs to enhance timeliness and power efficiency; (2) investigate a self-adaptive control framework that can maintain desired timeliness despite workload variations, using fewer processor cores to conserve power; and (3) explore how to leverage advanced memory hardware features and real-time data characteristics to further enhance timeliness and reduce processor and memory power consumption.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.

实时数据服务在支持智能交通、医疗、制造等数据密集型实时应用中发挥着至关重要的作用，具有巨大的社会价值。这些应用需要经济高效的实时嵌入式数据库，能够使用代表当前现实世界状态的新数据及时处理实时数据服务请求，同时节省电力。该项目旨在研究新的方法，以促进这一重要但尚未充分探索的研究领域的科学进步。此外，通过提高资源和电源效率，它旨在大大​​减轻在资源有限的深度嵌入式系统中部署实时数据服务的挑战。该项目的更广泛影响包括培训本科生和研究生，包括代表性不足的学生群体、外展活动和开源软件。该研究项目的目标是通过调查来弥合节能实时数据服务的知识差距以数据为中心的方法可以提高及时性和能效，而不会影响两者。然而，由于多种因素，实现这一目标具有挑战性，包括基于当前现实世界状态的用户交易的不同到达率和数据需求、数据和资源争用，以及嵌入式系统中严格的时序、资源和功率限制。为了应对这些挑战，该研究将（1）研究一种新的有效的实时交易模型和以数据为中心的方法，以减少工作负载，识别实际数据需求和新鲜度需求，以提高及时性和能源效率； (2) 研究一种自适应控制框架，该框架可以在工作负载变化的情况下保持所需的及时性，并使用更少的处理器内核来节省电量； (3) 探索如何利用先进的内存硬件功能和实时数据特性，进一步提高时效性并降低处理器和内存功耗。该奖项体现了 NSF 的法定使命，并通过利用基金会的智力优势进行评估，认为值得支持以及更广泛的影响审查标准。