# Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies

arXiv: https://arxiv.org/abs/2103.05245 · HTML: https://mlsanity.com/paper/2103.05245/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Microservices: yesterday, today, and tomorrow](https://mlsanity.com/paper/1606.04036/) (2016)
- [Localizing Faults in Cloud Systems](https://mlsanity.com/paper/1803.00356/) (2018)

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

- [Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features](https://mlsanity.com/paper/2307.10869/) (2023)
- [Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments](https://mlsanity.com/paper/2211.13729/) (2022)

## Abstract (arXiv)

Operation and maintenance of large distributed cloud applications can quickly become unmanageably complex, putting human operators under immense stress when problems occur. Utilizing machine learning for identification and localization of anomalies in such systems supports human experts and enables fast mitigation. However, due to the various inter-dependencies of system components, anomalies do not only affect their origin but propagate through the distributed system. Taking this into account, we present Arvalus and its variant D-Arvalus, a neural graph transformation method that models system components as nodes and their dependencies and placement as edges to improve the identification and localization of anomalies. Given a series of metric KPIs, our method predicts the most likely system state - either normal or an anomaly class - and performs localization when an anomaly is detected. During our experiments, we simulate a distributed cloud application deployment and synthetically inject anomalies. The evaluation shows the generally good prediction performance of Arvalus and reveals the advantage of D-Arvalus which incorporates information about system component dependencies.