# Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation

arXiv: https://arxiv.org/abs/2009.12922 · HTML: https://mlsanity.com/paper/2009.12922/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Predictive Price-Performance Optimization for Serverless Query Processing](https://mlsanity.com/paper/2112.08572/) (2021)
- [Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling](https://mlsanity.com/paper/2508.12773/) (2025)
- [Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments](https://mlsanity.com/paper/2607.23503/) (2026)
- [QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization](https://mlsanity.com/paper/2609.20156/) (2026)

## Abstract (arXiv)

Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Data Science (DS) driven solution to predict user load and leverage these predictions to optimize resource allocation. To this end, we built the Seagull infrastructure that processes per-server telemetry, validates the data, trains and deploys ML models. The models are used to predict customer load per server (24h into the future), and optimize service operations. Seagull continually re-evaluates accuracy of predictions, fallback to previously known good models and triggers alerts as appropriate. We deployed this infrastructure in production for PostgreSQL and MySQL servers across all Azure regions, and applied it to the problem of scheduling server backups during low-load time. This minimizes interference with user-induced load and improves customer experience.