# Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads

arXiv: https://arxiv.org/abs/1708.07308 · HTML: https://mlsanity.com/paper/1708.07308/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2017.

## Built on by (most cited first, 16 of 16)

- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2107.08861/) (2021)
- [xRouter: Training Cost-Aware LLMs Orchestration System via Reinforcement Learning](https://mlsanity.com/paper/2510.08439/) (2025)
- [SubStrat: A Subset-Based Strategy for Faster AutoML](https://mlsanity.com/paper/2206.03070/) (2022)
- [Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision](https://mlsanity.com/paper/2205.11913/) (2022)
- [Scavenger: A Cloud Service for Optimizing Cost and Performance of ML Training](https://mlsanity.com/paper/2303.06659/) (2023)
- [Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks](https://mlsanity.com/paper/2311.06028/) (2023)
- [SHiFT: An Efficient, Flexible Search Engine for Transfer Learning](https://mlsanity.com/paper/2204.01457/) (2022)
- [LoRAFusion: Efficient LoRA Fine-Tuning for LLMs](https://mlsanity.com/paper/2510.00206/) (2025)
- [fairDMS: Rapid Model Training by Data and Model Reuse](https://mlsanity.com/paper/2204.09805/) (2022)
- [TENSILE: A Tensor granularity dynamic GPU memory scheduling method toward multiple dynamic workloads system](https://mlsanity.com/paper/2105.13336/) (2021)
- [Budget-aware Query Tuning: An AutoML Perspective](https://mlsanity.com/paper/2404.00137/) (2024)
- [Introducing 'Inside' Out of Distribution](https://mlsanity.com/paper/2407.04534/) (2024)
- [A Simulation Platform for Multi-tenant Machine Learning Services on Thousands of GPUs](https://mlsanity.com/paper/2201.03175/) (2022)
- [Efficient End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2206.09423/) (2022)
- [Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design](https://mlsanity.com/paper/2507.15336/) (2025)

## Abstract (arXiv)

We present ease.ml, a declarative machine learning service platform we built to support more than ten research groups outside the computer science departments at ETH Zurich for their machine learning needs. With ease.ml, a user defines the high-level schema of a machine learning application and submits the task via a Web interface. The system automatically deals with the rest, such as model selection and data movement. In this paper, we describe the ease.ml architecture and focus on a novel technical problem introduced by ease.ml regarding resource allocation. We ask, as a "service provider" that manages a shared cluster of machines among all our users running machine learning workloads, what is the resource allocation strategy that maximizes the global satisfaction of all our users? Resource allocation is a critical yet subtle issue in this multi-tenant scenario, as we have to balance between efficiency and fairness. We first formalize the problem that we call multi-tenant model selection, aiming for minimizing the total regret of all users running automatic model selection tasks. We then develop a novel algorithm that combines multi-armed bandits with Bayesian optimization and prove a regret bound under the multi-tenant setting. Finally, we report our evaluation of ease.ml on synthetic data and on one service we are providing to our users, namely, image classification with deep neural networks. Our experimental evaluation results show that our proposed solution can be up to 9.8x faster in achieving the same global quality for all users as the two popular heuristics used by our users before ease.ml.