It is challenging to process transactions in a timely fashion using fresh data, e.g., current

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.

The integration phase of real-time COTS-based

A lot of real-time database (RTDB) research has been done to process transactions in a timely fashion using fresh data reflecting the current real world status. However, most existing RTDB work is based on simulations. Due to the absence of a publicly available RTDB testbed, it is very hard to evaluate real-time data management techniques in a realistic environment. To address the problem, we design and develop an initial version of a RTDB testbed, called RTDB2 (Real-Time Database Benchmark), atop an open source database [5]. We develop soft real-time database workloads that model online stock trades, providing several knobs to specify workloads for RTDB performance evaluation. In addition, we develop a QoS management scheme in RTDB2 to detect overload and reduce workloads, via admission control and adaptive temporal data updates, under overload. From the extensive experiments using the stock trading workloads developed in RTDB2, we observe that adaptive updates can considerably improve the transaction timeliness. We also observe that admission control can only enhance the timeliness under severe overload, possibly causing underutilization problems for moderate workloads.

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It is challenging to manage the performance of real-time databases (RTDBs) that are often used in data-intensive real-time applications such as agile manufacturing and target tracking. Feedback control has recently been considered a promising approach to enabling reliable real-time data service. However, most existing work on feedback control of RTDB performance is not based on a RTDB-specific control model, which is critical for closed-loop system design. To address this problem, we design a novel RTDB model that can capture RTDB dynamics by modeling the relation between the total arrival rate (sum of the transaction arrival rate and restart rate) and utilization via a difference equation. Based on the model, we design and tune a utilization controller to compute the total arrival rate adjustment needed to support the desired average/transient utilization. We also design a QoS management scheme and admission control technique that can judiciously adapt the transaction QoS and arrival rate in a RTDB, if necessary, to support the desired utilization, while enhancing the success ratio. In a simulation study, we show that our approach can support the desired average/transient utilization for a range of transaction arrival rates under severe data contention, while considerably improving the success ratio compared to the tested baselines.