Approximate Quantiles for Datacenter Telemetry Monitoring

Abstract

Datacenter systems require real-time troubleshooting so as to minimize downtimes. In doing so, datacenter operators employ streaming analytics for collecting and processing datacenter telemetry over a temporal window. Quantile computation is key to this telemetry monitoring since it can summarize the typical and abnormal behavior of the monitored system. However, computing quantiles in real-time is resource-intensive as it requires processing hundreds of millions of events in seconds while providing high accuracy. To address these challenges, we propose AOMG, an efficient and accurate quantile approximation algorithm that capitalizes insights from our workload study. AOMG improves performance through two-level hierarchical windowing while offering small value errors in a wide range of quantiles by taking into account the density of underlying data distribution. Our evaluations show that AOMG estimates the exact quantiles with less than 5% relative value error for a variety of use cases while providing high throughput.