Tejo: A Supervised Anomaly Detection Scheme for NewSQL Databases

摘要

The increasing availability of streams of data and the need of auto-tuning applications have made big data mainstream. NewSQL databases have become increasingly important to ensure fast data processing for the emerging stream processing platforms. While many architectural improvements have been made on NewSQL databases to handle fast data processing, anomalous events on the underlying, complex cloud environments may undermine their performance. In this paper, we present Tejo, a supervised anomaly detection scheme for NewSQL databases. Unlike general-purpose anomaly detection for the cloud, Tejo characterizes anomalies in NewSQL database clusters based on Service Level Objective (SLO) metrics. Our experiments with VoltDB, a prominent NewSQL database, shed some light on the impact of anomalies on these databases and highlight the key design choices to enhance anomaly detection.