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Towards evolutionary machine learning comparison, competition, and collaboration with a multi-cloud platform

Authors:

Pasquale Salza,

Erik Hemberg,

Filomena Ferrucci,

Una-May O'ReillyAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1263 - 1270

https://doi.org/10.1145/3067695.3082474

Published: 15 July 2017 Publication History

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Abstract

We present cCube, an open source architecture used to automatically create an application of one or more Evolutionary Machine Learning (EML) classification algorithms that can be deployed to the cloud with automatic data factorization, training, result filtering and fusion. cCube enables automated EML classification algorithms comparison, competition and multi-party collaboration. It can be used by an algorithm developer, a community working together or a black box user of EML classification. It requires minimal extra code to cloud-scale shared-memory implementations. It employs a microservices architecture and software containers into which user code is integrated allowing to access to the full benefits of cloud computing, e.g., on demand and elastic computing, while not committing (code or patronage) to a specific cloud provider such as Amazon Web Services or OpenStack. We demonstrate cCube, straddling our application across two different cloud providers and replicate the collaborative activity at zero cost.

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Towards evolutionary machine learning comparison, competition, and collaboration with a multi-cloud platform

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GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 July 2017

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GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

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Park HQiu ZGanger GAmvrosiadis GWitchel EArpaci-Dusseau ARossbach CKeeton K(2024)Reducing Cross-Cloud/Region Costs with the Auto-Configuring MACARON CacheProceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles10.1145/3694715.3695972(347-368)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3694715.3695972
Opara EWimmer HRebman C(2022)Auto-ML Cyber Security Data Analysis Using Google, Azure and IBM Cloud Platforms2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)10.1109/ICECET55527.2022.9872782(1-10)Online publication date: 20-Jul-2022
https://doi.org/10.1109/ICECET55527.2022.9872782
John MHolmström Olsson HBosch J(2021)Architecting AI Deployment: A Systematic Review of State-of-the-Art and State-of-Practice LiteratureSoftware Business10.1007/978-3-030-67292-8_2(14-29)Online publication date: 22-Jan-2021
https://doi.org/10.1007/978-3-030-67292-8_2
Talamo CPaganin GRota F(2019)Industry 4.0 for failure information management within Proactive MaintenanceIOP Conference Series: Earth and Environmental Science10.1088/1755-1315/296/1/012055296(012055)Online publication date: 30-Jul-2019
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Dziurzanski PSwan JIndrusiak LTakadama K(2018)Value-based manufacturing optimisation in serverless clouds for industry 4.0Proceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205501(1222-1229)Online publication date: 2-Jul-2018
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cCube: a cloud microservices architecture for evolutionary machine learning classification

Monitoring-based auto-scalability across hybrid clouds

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