# ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB

arXiv: https://arxiv.org/abs/2608.07945 · HTML: https://mlsanity.com/paper/2608.07945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Bao: Learning to Steer Query Optimizers](https://mlsanity.com/paper/2004.03814/) (2020)
- [Lero: A Learning-to-Rank Query Optimizer](https://mlsanity.com/paper/2302.06873/) (2023)
- [Auto-scaling Web Applications in Clouds: A Taxonomy and Survey](https://mlsanity.com/paper/1609.09224/) (2016)
- [Forecasting SQL Query Cost at Twitter](https://mlsanity.com/paper/2204.05529/) (2022)
- [Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud](https://mlsanity.com/paper/2208.04978/) (2022)
- [Cost-Intelligent Data Analytics in the Cloud](https://mlsanity.com/paper/2308.09569/) (2023)
- [Lorentz: Learned SKU Recommendation Using Profile Data](https://mlsanity.com/paper/2411.11325/) (2024)
- [Evaluating Learned Query Performance Prediction Models at LinkedIn: Challenges, Opportunities, and Findings](https://mlsanity.com/paper/2504.17181/) (2025)
- [SafeLoad: Efficient Admission Control Framework for Identifying Memory-Overloading Queries in Cloud Data Warehouses](https://mlsanity.com/paper/2601.01888/) (2026)

## Abstract (arXiv)

Cloud-native serverless data warehouses achieve fine-grained elasticity by decoupling storage from compute, yet determining the optimal resource allocation for highly heterogeneous ad-hoc queries remains a formidable industrial challenge. Our analysis of production workloads in Alibaba AnalyticDB exposes a costly ``provisioning trap'': the fear of catastrophic resource depletion drives users to blindly over-provision resources, wasting immense monetary budgets without alleviating non-CPU bottlenecks (e.g., I/O saturation). To break this impasse, we propose ScaleSense, a proactive, query-level resource scaling framework. Specifically, it features a multi-faceted query encoder that jointly models plan topologies and hardware specifications. Crucially, a quantile-based resource predictor estimates multi-dimensional physical footprints, acting as a reliable safety net for optimal resource scaling. An auto-scaling controller then navigates the performance-cost Pareto frontier, dynamically tailoring allocations to specific business priorities without requiring model retraining. Evaluations on over 1.36 million production queries show that ScaleSense achieves state-of-the-art prediction accuracy with good prediction interval coverage. By achieving a 76.7% relative improvement in optimal resource configuration selection over the best baseline, this approach addresses the critical performance-cost trade-off while maintaining low-overhead inference latency, confirming its practical performance in production deployments. Under the performance-optimization policy, ScaleSense satisfies user-defined performance requirements while reducing monetary cost by up to 5.22x.