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

实时数据服务在支持数据密集型实时应用方面发挥着至关重要的作用，例如智能交通、医疗保健和制造业，具有重大的社会价值。这些应用程序需要具有成本效益的实时嵌入式数据库，这些数据库能够使用代表当前真实世界状态的新鲜数据，及时处理实时数据服务请求，同时还可以节省电力。该项目旨在调查新的方法，以促进这一基本但未被探索的研究领域的科学进步。此外，通过提高资源和能源效率，它旨在极大地缓解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毕业生，包括代表性不足的学生群体、外联活动和开源软件。该研究项目的目标是通过调查以数据为中心的方法，在不影响两者的前提下，弥合高能效实时数据服务方面的知识差距。然而，由于几个因素，包括基于当前真实世界状态的用户事务的不同到达速率和数据需求、数据和资源争用，以及嵌入式系统中严格的定时、资源和功率限制，实现这一目标是具有挑战性的。为了应对这些挑战，这项研究将(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来减少工作负载，以识别实际数据需求和新鲜度需求，以提高时效性和能效；(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期望的时效性，使用更少的处理器核心来节省电力；以及(3)探索如何利用高级内存硬件功能和实时数据特征来进一步增强时效性，并降低处理器和内存功耗。该奖项反映了NSF的法定使命，并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。

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.