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Daleel: simplifying cloud instance selection using machine learning

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Daleel: simplifying cloud instance selection using machine learning. / Samreen, Faiza; El Khatib, Yehia; Rowe, Matthew Charles et al.
Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP. IEEE, 2016. (Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Samreen, F, El Khatib, Y, Rowe, MC & Blair, GS 2016, Daleel: simplifying cloud instance selection using machine learning. in Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP. Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP, IEEE. https://doi.org/10.1109/NOMS.2016.7502858

APA

Samreen, F., El Khatib, Y., Rowe, M. C., & Blair, G. S. (2016). Daleel: simplifying cloud instance selection using machine learning. In Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP (Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP). IEEE. https://doi.org/10.1109/NOMS.2016.7502858

Vancouver

Samreen F, El Khatib Y, Rowe MC, Blair GS. Daleel: simplifying cloud instance selection using machine learning. In Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP. IEEE. 2016. (Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP). doi: 10.1109/NOMS.2016.7502858

Author

Samreen, Faiza ; El Khatib, Yehia ; Rowe, Matthew Charles et al. / Daleel : simplifying cloud instance selection using machine learning. Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP. IEEE, 2016. (Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP).

Bibtex

@inproceedings{ebfc7656c5e741ad9d92726828998ff2,
title = "Daleel: simplifying cloud instance selection using machine learning",
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.",
keywords = "Cloud computing, Machine learning",
author = "Faiza Samreen and {El Khatib}, Yehia and Rowe, {Matthew Charles} and Blair, {Gordon Shaw}",
year = "2016",
month = apr,
day = "25",
doi = "10.1109/NOMS.2016.7502858",
language = "English",
series = "Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP",
publisher = "IEEE",
booktitle = "Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP",

}

RIS

TY - GEN

T1 - Daleel

T2 - simplifying cloud instance selection using machine learning

AU - Samreen, Faiza

AU - El Khatib, Yehia

AU - Rowe, Matthew Charles

AU - Blair, Gordon Shaw

PY - 2016/4/25

Y1 - 2016/4/25

N2 - 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.

AB - 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.

KW - Cloud computing

KW - Machine learning

U2 - 10.1109/NOMS.2016.7502858

DO - 10.1109/NOMS.2016.7502858

M3 - Conference contribution/Paper

T3 - Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP

BT - Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP

PB - IEEE

ER -