Integrating Proactive and Reactive Approaches for Robust Real-Time Data Services

Abstract

Abstract-Real-time data services are needed in data-intensive real-time applications such as e-commerce or traffic control. However, it is challenging to support real-time data services, if workloads dynamically change based on the market or traffic status. To enhance the quality of real-time data services even in the presence of dynamic workloads, feedback control theory has been applied. However, a major drawback of feedback control is that it only reacts to performance errors. To improve the robustness of realtime data services, we develop a statistical feed-forward approach that proactively adapts the incoming load, if necessary, to support the desired real-time data service delay. Further, we integrate it with a feedback controller to compensate potential prediction errors and adjust the system behavior in a reactive manner for timely data services. Performance evaluation results acquired in our real-time data service testbed show that our integrated approach considerably reduces the average delay and transient delay fluctuations, while improving throughput compared to the tested baselines including the feed-forward-only and feedbackonly approaches.

Dynamic Environment and Snowflake Schema in Real Time Data Services

“…Step 3: CLT+FB [7] it provides service delay greater than FB at 2.2second. But we will want a lower service delay.…”

“…CLT+FBControl +FeedBack. It will combine the statistical feedforward [7] approach it will refer the center limit theorem [8]. It will be alter the database server in a timely manner to the freshness of the data.…”

“…Generally , RTDBs occasionally update profane data, e.g., Online-stock prices, to support the proven reliability of data within their trusted intervals [2], [3]. DSMS [5], [6], [7], [8] that data are coming in a rupture comportment and implement them connected in sequence of inquiry upon data influx. Sustaining the preferred well-timed data is critical to achieve, because real-time data ritual requests may arrive in a spurt manner, i.e., Changing stock query status.…”