# AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes

arXiv: https://arxiv.org/abs/2303.03640 · HTML: https://mlsanity.com/paper/2303.03640/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series](https://mlsanity.com/paper/1812.01767/) (2018)
- [Serverless Computing: Current Trends and Open Problems](https://mlsanity.com/paper/1706.03178/) (2017)
- [RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering](https://mlsanity.com/paper/1906.03751/) (2019)
- [RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection](https://mlsanity.com/paper/2002.09535/) (2020)
- [Auto-scaling Web Applications in Clouds: A Taxonomy and Survey](https://mlsanity.com/paper/1609.09224/) (2016)
- [A simple and effective predictive resource scaling heuristic for large-scale cloud applications](https://mlsanity.com/paper/2008.01215/) (2020)

## Built on by (most cited first, 9 of 9)

- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting](https://mlsanity.com/paper/2405.06419/) (2024)
- [OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud](https://mlsanity.com/paper/2311.12864/) (2023)
- [Streamlining Resilient Kubernetes Autoscaling with Multi-Agent Systems via an Automated Online Design Framework](https://mlsanity.com/paper/2505.21559/) (2025)
- [Analytically-Driven Resource Management for Cloud-Native Microservices](https://mlsanity.com/paper/2401.02920/) (2024)
- [Leveraging Interpretability in the Transformer to Automate the Proactive Scaling of Cloud Resources](https://mlsanity.com/paper/2409.03103/) (2024)
- [Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling](https://mlsanity.com/paper/2508.12773/) (2025)
- [STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices](https://mlsanity.com/paper/2604.26422/) (2026)
- [STARIXNet: Multivariate and Multi-attribute Deep Learning Approach to Real-Time Resource Allocation in Cloud Platforms](https://mlsanity.com/paper/2606.07565/) (2026)

## Abstract (arXiv)

The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous waste of resources during fluctuations. Moreover, the emergence of new cloud services puts higher resource management requirements. This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system. Based on a robust decomposition forecasting algorithm and performance training model, AHPA offers an optimal pod number adjustment plan that could reduce POD resources and maintain business stability. Since being deployed in April 2021, this system has expanded to multiple customer scenarios, including logistics, social networks, AI audio and video, e-commerce, etc. Compared with the previous algorithms, AHPA solves the elastic lag problem, increasing CPU usage by 10% and reducing resource cost by more than 20%. In addition, AHPA can automatically perform flexible planning according to the predicted business volume without manual intervention, significantly saving operation and maintenance costs.