CSR---EHS: Collaborative Research: QoS-Aware Data Services in Data-Intensive Real-Time Embedded Applications

在数据密集型实时嵌入式应用（如交通控制、敏捷制造和国防应用）中，使用反映当前真实的世界状态的新鲜时态数据及时处理实时事务是必不可少的。本研究的首要目标是开发一种新的理论框架和原型RTEDB（实时嵌入式数据库）系统，可以支持所需的事务的及时性和数据的新鲜度，即使在存在数据/资源争用和动态工作负载。该项目正在开发有效的服务质量（QoS）指标，新的控制理论模型，反馈控制和前馈方法，以指定所需的QoS，模型RTEDB动态，并支持所需的QoS的数学模型的基础上，同时防止严重的数据冲突。该研究正在开发几种新的工作负载调整技术，包括自适应时态数据更新，自适应并发控制，准入控制和服务区分技术，优雅地适应和区分实时数据服务，对用户感知的QoS影响最小。为了提高实时数据服务的可扩展性，研究了控制区域内的负载均衡和时态数据复制。该研究还在开发新的方法，以实现实时嵌入式应用中高效、安全的数据传输。此外，研究团队致力于将这一前沿研究与教学和推广计划相结合。该项目，如果成功的话，最终设想QoS感知RTEDB将用于一些重要的数据密集型实时嵌入式应用程序。

Timely processing of real-time transactions using fresh temporal data, which reflect the current real world status, is essential in data-intensive real-time embedded applications such as traffic control, agile manufacturing, and defense applications. An overarching goal of this research is developing a novel theoretic framework and prototypical RTEDB (Real-Time Embedded Database) system that can support the desired transaction timeliness and data freshness even in the presence of data/resource contention and dynamic workloads. This project is developing effective Quality of Service (QoS) metrics, novel control theoretic models, feedback control, and feed-forward approaches to specify the desired QoS, model RTEDB dynamics, and support the desired QoS based on the mathematical model, while preventing severe data conflicts. The research is developing several new workload adjustment techniques including adaptive temporal data update, adaptive concurrency control, admission control, and service differentiation techniques to gracefully adapt and differentiate real-time data service with minimal impacts on the user perceived QoS. To improve the scalability of real-time data service, the research focuses on load balancing and temporal data replication in a control area. The research also is developing novel approaches for efficient, secure data transmission in real-time embedded applications. Moreover, the research team is committed to integrating this leading-edge research with teaching and outreach programs. The project, if successful, ultimately envisions that QoS-aware RTEDBs will be used in a number of important data-intensive real-time embedded applications.