TurboProm speeds up metrics monitoring while cutting cloud costs
TurboProm: Low Cost and High Performance Metrics Observability at Scale
DatabasesDistributed, Parallel, and Cluster ComputingNetworking and Internet Architecture
Summary
Many big companies use tools like Prometheus to monitor how their computer systems are working by collecting lots of data points. But handling and analyzing this data quickly and cheaply can be hard when there is a lot of it. The authors present TurboProm, a tool that can be added to existing systems without changing them, which summarizes this data in a smart way to keep the details accurate while making queries faster and cheaper. They tested TurboProm with real and simulated data and found it reduces delays and costs significantly compared to current solutions.
What this means in practice
- •For cloud operations teams: Reduce the cost and speed up querying of system health metrics without changing existing monitoring setups.
- •For data center infrastructure managers: Improve efficiency of analyzing large-scale performance data to detect system issues faster and at lower expense.
Authors
Milind Srivastava, Zeying Zhu, Peilin Xin, Yancheng Yuan, Zaoxing Liu, Vyas Sekar
Abstract
Many large enterprise and cloud deployments rely on metrics observability solutions (e.g., Prometheus) to monitor key performance indicators of system health (e.g., CPU, memory, network, app-level statistics). Monitoring and analyzing these metrics presents a significant challenge in terms of cost-latency tradeoffs at scale. Ideally, system operators would like immediately deployable and backwards compatible solutions to improve the cost-latency tradeoffs without significant impact on the fidelity of their observability applications. Unfortunately, existing solutions including state-of-art timeseries databases and custom approximate querying solutions fail on one or more aspects. In this paper, we present the design and implementation of TurboProm, a backwards-compatible drop-in accelerator for metrics observability deployments. TurboProm recasts metrics analytics tasks using a semantic-preserving summary view rather than classical query-centric view. Instead of estimating query results on raw timeseries metrics data, TurboProm uses semantic-preserving summaries that enable high-fidelity analytics with significant cost-latency improvements. We present a practical implementation of TurboProm that can be retrofitted to any existing observability deployment. We evaluate TurboProm on diverse real and synthetic workloads against state-of-art solutions and show that it can provide significant reduction in query latency and cost. TurboProm is open-source at https://github.com/ProjectASAP/ASAPQuery.