FIRED: a fine-grained robust performance diagnosis framework for cloud applications

TL;DR

FIRED is a framework that improves performance anomaly detection and root cause localization in cloud applications using ensemble deep learning and fine-grained analysis, achieving high accuracy with limited labels.

Contribution

The paper introduces FIRED, a novel framework combining ensemble models and weakly-supervised learning for robust, fine-grained performance diagnosis in cloud environments.

Findings

Achieves anomaly detection F1 score > 0.8 within 4 minutes.

Locates first four root causes with over 70% accuracy.

Outperforms existing models in robustness and re-usability.

Abstract

To run a cloud application with the required service quality, operators have to continuously monitor the cloud application's run-time status, detect potential performance anomalies, and diagnose the root causes of anomalies. However, existing models of performance anomaly detection often suffer from low re-usability and robustness due to the diversity of system-level metrics being monitored and the lack of high-quality labeled monitoring data for anomalies. Moreover, the current coarse-grained analysis models make it difficult to locate system-level root causes of the application performance anomalies for effective adaptation decisions. We provide a FIne-grained Robust pErformance Diagnosis (FIRED) framework to tackle those challenges. The framework offers an ensemble of several well-selected base models for anomaly detection using a deep neural network, which adopts weakly-supervised learning considering fewer labels exist in reality. The framework also employs a real-time fine-grained analysis model to locate dependent system metrics of the anomaly. Our experiments show that the framework can achieve the best detection accuracy and algorithm robustness, and it can predict anomalies in four minutes with F1 score higher than 0.8. In addition, the framework can accurately localize the first root causes, and with an average accuracy higher than 0.7 of locating first four root causes.