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It is challenging to process real-time data service requests, such as online trade or traffic monitoring requests, within their deadlines, while providing differentiated real-time data services. To address the problem, we present new approaches to 1) assigning deadlines to real-time data service requests based on their data access needs and service classes to differentiate real-time data services when the system is busy and 2) closely supporting the specified target delay to deadline ratio (DDR)—the ratio of actual data service delays to deadlines—via feedback control even in the presence of dynamic workloads. Further, we have actually implemented our approaches by extending an open source database unlike most existing work on real-time databases. The experimental results show that our approach closely supports the target DDR bound and service differentiation requirements among the service classes unlike the tested baselines representing the current state of the art in real-time database research. [ABSTRACT FROM PUBLISHER]

Supporting timely data services using fresh data in data-intensive real-time applications, such as e-commerce and transportation management is desirable but challenging, since the workload may vary dynamically. To control the data service delay to be below the specified threshold, we develop a predictive as well as reactive method for database admission control. The predictive method derives the workload bound for admission control in a predictive manner, making no statistical or queuing-theoretic assumptions about workloads. Also, our reactive scheme based on formal feedback control theory continuously adjusts the database load bound to support the delay threshold. By adapting the load bound in a proactive fashion, we attempt to avoid severe overload conditions and excessive delays before they occur. Also, the feedback control scheme enhances the timeliness by compensating for potential prediction errors due to dynamic workloads. Hence, the predictive and reactive methods complement each other, enhancing the robustness of real-time data services as a whole. We implement the integrated approach and several baselines in an open-source database. Compared to the tested open-loop, feedback-only, and statistical prediction + feedback baselines representing the state of the art, our integrated method significantly improves the average/transient delay and real-time data service throughput. [ABSTRACT FROM AUTHOR]

It is essential to process real-time data service requests such as stock quotes and trade transactions in a timely manner using fresh data, which represent the current real-world phenomena such as the stock market status. Users may simply leave when the database service delay is excessive. Also, temporally inconsistent data may give an outdated view of the real-world status. However, supporting the desired timeliness and freshness is challenging due to dynamic workloads. To address the problem, we present new approaches for 1) database backlog estimation, 2) fine-grained closed-loop admission control based on the backlog model, and 3) incoming load smoothing. Our backlog estimation and control-theoretic approaches aim to support the desired service delay bound without degrading the data freshness, critical for real-time data services. Specifically, we design, implement, and evaluate two feedback controllers based on linear control theory and fuzzy logic control theory, to meet the desired service delay. Workload smoothing, under overload, helps the database admit and process more transactions in a timely fashion by probabilistically reducing the burstiness of incoming data service requests. In terms of the data service delay and throughput, our closed-loop admission control and probabilistic load smoothing schemes considerably outperform several baselines in the experiments undertaken in a stock trading database testbed. [ABSTRACT FROM PUBLISHER]