# Sinan: Data-Driven, QoS-Aware Cluster Management for Microservices

arXiv: https://arxiv.org/abs/2105.13424 · HTML: https://mlsanity.com/paper/2105.13424/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015)
- [Auto-scaling Web Applications in Clouds: A Taxonomy and Survey](https://mlsanity.com/paper/1609.09224/) (2016)
- [Overload Control for Scaling WeChat Microservices](https://mlsanity.com/paper/1806.04075/) (2018)
- [How Constraints Affect Content: The Case of Twitter's Switch from 140 to 280 Characters](https://mlsanity.com/paper/1804.02318/) (2018)
- [The Architectural Implications of Microservices in the Cloud](https://mlsanity.com/paper/1805.10351/) (2018)

## Abstract (arXiv)

Cloud applications are increasingly shifting from large monolithic services, to large numbers of loosely-coupled, specialized microservices. Despite their advantages in terms of facilitating development, deployment, modularity, and isolation, microservices complicate resource management, as dependencies between them introduce backpressure effects and cascading QoS violations. We present Sinan, a data-driven cluster manager for interactive cloud microservices that is online and QoS-aware. Sinan leverages a set of scalable and validated machine learning models to determine the performance impact of dependencies between microservices, and allocate appropriate resources per tier in a way that preserves the end-to-end tail latency target. We evaluate Sinan both on dedicated local clusters and large-scale deployments on Google Compute Engine (GCE) across representative end-to-end applications built with microservices, such as social networks and hotel reservation sites. We show that Sinan always meets QoS, while also maintaining cluster utilization high, in contrast to prior work which leads to unpredictable performance or sacrifices resource efficiency. Furthermore, the techniques in Sinan are explainable, meaning that cloud operators can yield insights from the ML models on how to better deploy and design their applications to reduce unpredictable performance.