# Daleel: Simplifying Cloud Instance Selection Using Machine Learning

arXiv: https://arxiv.org/abs/1602.02159 · HTML: https://mlsanity.com/paper/1602.02159/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2016.

## Built on by (most cited first, 1 of 1)

- [On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation](https://mlsanity.com/paper/2107.02466/) (2021)

## Abstract (arXiv)

Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one. In addition, there are no hard and fast rules, each customer has a specific set of constraints (e.g. budget) and application requirements (e.g. minimum computational resources). Machine learning can help address some of the complicated decisions by carrying out customer-specific analytics to determine the most suitable instance type(s) and the most opportune time for starting or migrating instances. We employ machine learning techniques to develop an adaptive deployment policy, providing an optimal match between the customer demands and the available cloud service offerings. We provide an experimental study based on extensive set of job executions over a major public cloud infrastructure.