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

实时数据服务在支持具有重大社会价值的数据密集型实时应用（如智能交通、医疗保健和制造业）方面发挥着至关重要的作用。这些应用程序需要具有成本效益的实时嵌入式数据库，能够及时处理实时数据服务请求，使用代表当前现实世界状态的新数据，同时还节省电力。该项目旨在研究新的方法，以促进这一重要但尚未开发的研究领域的科学进步。此外，通过提高资源和电力效率，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。该项目更广泛的影响包括培训本科生和毕业生，包括代表性不足的学生群体、拓展活动和开源软件。本研究项目的目标是通过研究以数据为中心的方法来弥合节能实时数据服务方面的知识鸿沟，这些方法既可以提高及时性，又可以提高能效，同时不会影响两者。然而，由于几个因素，实现这一目标是具有挑战性的，包括基于当前现实世界状态、数据和资源争用的用户事务的不同到达率和数据需求，以及嵌入式系统中严格的时间、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事务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，以提高及时性和电力效率；(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时效性，使用更少的处理器内核来节省功率；(3)探索如何利用先进的内存硬件特性和实时数据特性，进一步提高时效性，降低处理器和内存功耗。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.