Real-time data services can benefit data-intensive real-time applications, e.g., e-commerce, via timely transaction processing using fresh data, e.g., the current stock prices. To enhance the real-time data service quality, we present several novel techniques for (1) database backlog estimation, (2) fine-grained closedloop admission control based on the backlog model, and (3) hint-based incoming load smoothing. Our backlog estimation and feedback control aim to support the desired service delay bound without degrading the data freshness critical for real-time data services. Workload smoothing, under overload, help the database admit and process more transactions in a timely manner by probabilistically reducing the burstiness of incoming data service requests. In terms of the data service delay and throughput, our feedback-based admission control and probabilistic load smoothing considerably outperform the baselines, which represent the current state of the art, in the experiments performed in a stock trading database testbed.

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The original publication of this paper unfortunately contained an error. The word "thydimine" in the Article title must be replaced with "thymidine".The online version of the original article can be found at http://dx.doi.

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It is challenging to support the timeliness of realtime data service requests in data-intensive real-time applications such as online auction or stock trading, while maintaining the freshness of temporal data that capture the current real-world status. Although deadline-aware real-time scheduling would significantly enhance the timeliness of data services, it is not clear how to assign explicit feasible deadlines to data service requests in an open environment. To address the problem, we design a new deadline assignment scheme to derive feasible deadlines for real-time data service requests considering their individual data needs. Further, we develop a systematic closed-loop approach to supporting the desired tardiness−the actual service delay to deadline ratio−of real-time data services even in the presence of dynamic workloads. We choose the tardiness metric due to its expressiveness compared to the deadline miss ratio and utilization that saturate at 0 and 1 when the system is underutilized or overloaded, respectively. The performance evaluation results acquired in our real-time stock trading testbed show that the desired average/transient tardiness is closely supported. Consequently, the deadline miss ratio is significantly reduced compared to a state-of-art database system with a real-time scheduling extension.

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