Daleel: Simplifying Cloud Instance Selection Using Machine Learning

TL;DR

Daleel leverages machine learning to simplify cloud instance selection by providing an adaptive deployment policy tailored to customer constraints and application needs, based on extensive experimental validation.

Contribution

The paper introduces a machine learning-based approach for optimizing cloud instance selection and deployment timing, addressing the complexity of diverse cloud offerings and customer requirements.

Findings

Effective matching of customer demands with cloud offerings

Improved decision-making for instance deployment timing

Validated approach through extensive experiments on public cloud

Abstract

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.