# Sage: Using Unsupervised Learning for Scalable Performance Debugging in Microservices

arXiv: https://arxiv.org/abs/2101.00267 · HTML: https://mlsanity.com/paper/2101.00267/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2021.

## Built on by (most cited first, 2 of 2)

- [The Multi-Agent Fault Localization System Based on Monte Carlo Tree Search Approach](https://mlsanity.com/paper/2507.22800/) (2025)
- [SLIM: a Scalable Light-weight Root Cause Analysis for Imbalanced Data in Microservice](https://mlsanity.com/paper/2405.20848/) (2024)

## Abstract (arXiv)

Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity microservices offer, they also complicate cluster management and performance debugging, as dependencies between tiers introduce backpressure and cascading QoS violations. We present Sage, a machine learning-driven root cause analysis system for interactive cloud microservices. Sage leverages unsupervised ML models to circumvent the overhead of trace labeling, captures the impact of dependencies between microservices to determine the root cause of unpredictable performance online, and applies corrective actions to recover a cloud service's QoS. In experiments on both dedicated local clusters and large clusters on Google Compute Engine we show that Sage consistently achieves over 93% accuracy in correctly identifying the root cause of QoS violations, and improves performance predictability.