Chronos: Feedback Control of a Real Database System Performance

It is challenging to process transactions in a timely fashion using fresh data, e.g., current stock prices, since database workloads may considerably vary due to dynamic data/resource contention. Further, transaction timeliness and data freshness requirements may compete for system resources. In this paper, we propose a novel feedback control model to support the desired data service delay by managing the size of the ready queue, which indicates the amount of the backlog in the database. We also propose a new self-adaptive update policy to adapt the freshness of cold data in a differentiated manner based on temporal data access and update patterns. Unlike most existing work on feedback control of real-time database (RTDB) performance, we actually implement and evaluate feedback control and database workload adaptation techniques in a real database testbed modeling stock trades. For performance evaluation, we undertake experiments in the testbed, which consists of thousands of client threads concurrently requesting database services for stock quotes, trades, and portfolio updates in a bursty manner. In these experiments, our database system supports the desired response time bound and data freshness, while processing a significantly larger number of transactions in time compared to the tested baselines.

1 Introduction

Real-time databases (RTDBs) aim to process transactions in a timely manner using fresh temporal data, e.g., current stock prices or traffic sensor data, representing the real world status. RTDBs can reduce the difficulty of developing data-intensive real-time applications such as stock trading, agile manufacturing, and traffic control, by supporting the logical and temporal consistency of data via transactions. Existing non-real-time databases are unaware of timing and data freshness (i.e., data temporal consistency) requirements, showing poor performance in these applications [18], [19].