Approximate Quantiles for Datacenter Telemetry Monitoring

TL;DR

This paper introduces AOMG, an efficient algorithm for real-time quantile approximation in datacenter telemetry, achieving high accuracy and throughput with minimal memory use by leveraging workload-driven insights.

Contribution

AOMG is a novel quantile approximation method that significantly reduces memory and computation while maintaining high accuracy, tailored for datacenter telemetry streaming analytics.

Findings

AOMG achieves less than 5% relative value error across various use cases.

AOMG outperforms state-of-the-art algorithms in throughput and accuracy.

AOMG reduces memory footprint through compression and summarization techniques.

Abstract

Datacenter systems require efficient troubleshooting and effective resource scheduling so as to minimize downtimes and to efficiently utilize limited resources. In doing so, datacenter operators employ streaming analytics for collecting and processing datacenter telemetry over a temporal window. The quantile operator is key to these systems as it can summarize the typical and abnormal behavior of the monitored system. Computing quantiles in real-time is resource-intensive as it requires processing hundreds of millions of events in seconds while providing high accuracy. We overcome these challenges in real-time quantile computation through workload-driven approximation, motivated by three insights in our study: (i) values are dominated by a set of recurring small values, (ii) distribution of small values is consistent across different time scales, and (iii) tail values are dominated by a small set of large values. That is, we propose AOMG, an efficient and accurate quantile approximation algorithm that capitalizes on these insights. AOMG minimizes memory footprint of the quantile operator via compression and frequency-based summarization of small values. While these summaries are stored and processed at sub-window granularity for memory efficiency, they can extend to compute quantiles on user-defined temporal windows. Low value error for tail quantiles is achieved by retaining a few tail values per subwindow. AOMG estimates quantiles with high throughput and less than 5% relative value error across a wide range of use cases while state-of-the-art algorithms either have a high relative value error (9.3-137.0%) or deliver lower throughput (15-92%).