Backlog Estimation and Management for Real-Time Data Services

Abstract

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 closed-loop 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.