Year: 2025
Type: conference-paper
Abstract: Cloud-native telemetry systems like Prometheus enable observability through rule queries-window- and dimension-based aggregations over time series data. However, these queries incur high operational costs and latency due to raw data ingestion, repeated computations, and expensive storage. In this demonstration, we revisit the potential of approximate analytics to improve efficiency across the telemetry pipeline. We propose a hybrid, sketch-based caching architecture that unifies optimizations across ingestion, query processing, and storage. By supporting approximate representations at each stage, our design achieves significant cost savings while maintaining high accuracy and responsiveness.
Authors: Zeying Zhu, Zaoxing Liu
Institution: University of Maryland, College Park
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
OpenAlex ID: W4414109559