# WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases

arXiv: https://arxiv.org/abs/1601.08221 · HTML: https://mlsanity.com/paper/1601.08221/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2016.

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

- [A Unified Transferable Model for ML-Enhanced DBMS](https://mlsanity.com/paper/2105.02418/) (2021)
- [Real-time Workload Pattern Analysis for Large-scale Cloud Databases](https://mlsanity.com/paper/2307.02626/) (2023)
- [Neural-based Modeling for Performance Tuning of Spark Data Analytics](https://mlsanity.com/paper/2101.08167/) (2021)
- [Piece of CAKE: Adaptive Execution Engines via Microsecond-Scale Learning](https://mlsanity.com/paper/2602.04181/) (2026)

## Abstract (arXiv)

Workload management for cloud databases must deal with the tasks of resource provisioning, query placement and query scheduling in a manner that meets the application's performance goals while minimizing the cost of using cloud resources. Existing solutions have approached these three challenges in isolation, and with only a particular type of performance goal in mind. In this paper, we introduce WiSeDB, a learning-based framework for generating holistic workload management solutions customized to application-defined performance metrics and workload characteristics. Our approach relies on supervised learning to train cost-effective decision tree models for guiding query placement, scheduling, and resource provisioning decisions. Applications can use these models for both batch and online scheduling of incoming workloads. A unique feature of our system is that it can adapt its offline model to stricter/looser performance goals with minimal re-training. This allows us to present alternative workload management strategies that address the typical performance vs. cost trade-off of cloud services. Experimental results show that our approach has very low training overhead while offering low cost strategies for a variety of performance goals and workload characteristics.