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  • Elhabbash2019sloml

    Rights statement: © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in UCC'19: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing 2019 https://dl.acm.org/doi/10.1145/3344341.3368805

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SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud applications

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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SLO-ML : A Language for Service Level Objective Modelling in Multi-cloud applications. / Elhabbash, Abdessalam; Jumagaliyev, Assylbek; Blair, Gordon; Elkhatib, Yehia.

Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing. IEEE/ACM, 2019. p. 241–250.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Elhabbash, A, Jumagaliyev, A, Blair, G & Elkhatib, Y 2019, SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud applications. in Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing. IEEE/ACM, pp. 241–250. https://doi.org/10.1145/3344341.3368805

APA

Elhabbash, A., Jumagaliyev, A., Blair, G., & Elkhatib, Y. (2019). SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud applications. In Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing (pp. 241–250). IEEE/ACM. https://doi.org/10.1145/3344341.3368805

Vancouver

Elhabbash A, Jumagaliyev A, Blair G, Elkhatib Y. SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud applications. In Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing. IEEE/ACM. 2019. p. 241–250 https://doi.org/10.1145/3344341.3368805

Author

Elhabbash, Abdessalam ; Jumagaliyev, Assylbek ; Blair, Gordon ; Elkhatib, Yehia. / SLO-ML : A Language for Service Level Objective Modelling in Multi-cloud applications. Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing. IEEE/ACM, 2019. pp. 241–250

Bibtex

@inproceedings{0aeba39403864769b545fb5528dceaf3,
title = "SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud applications",
abstract = "Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the current cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements. Subsequently, SLO-ML selects the services to honour the customer's requirements and generates the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We evaluate SLO-ML using an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess SLO-ML's overheads through empirical scalability tests. We express the promises of SLO-ML in terms of gained productivity and experienced usability, and we highlight its limitations by analysing it as application requirements grow.",
author = "Abdessalam Elhabbash and Assylbek Jumagaliyev and Gordon Blair and Yehia Elkhatib",
note = "{\textcopyright} ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in UCC'19: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing 2019 https://dl.acm.org/doi/10.1145/3344341.3368805",
year = "2019",
month = dec
day = "1",
doi = "10.1145/3344341.3368805",
language = "English",
isbn = "9781450368940",
pages = "241–250",
booktitle = "Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing",
publisher = "IEEE/ACM",

}

RIS

TY - GEN

T1 - SLO-ML

T2 - A Language for Service Level Objective Modelling in Multi-cloud applications

AU - Elhabbash, Abdessalam

AU - Jumagaliyev, Assylbek

AU - Blair, Gordon

AU - Elkhatib, Yehia

N1 - © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in UCC'19: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing 2019 https://dl.acm.org/doi/10.1145/3344341.3368805

PY - 2019/12/1

Y1 - 2019/12/1

N2 - Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the current cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements. Subsequently, SLO-ML selects the services to honour the customer's requirements and generates the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We evaluate SLO-ML using an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess SLO-ML's overheads through empirical scalability tests. We express the promises of SLO-ML in terms of gained productivity and experienced usability, and we highlight its limitations by analysing it as application requirements grow.

AB - Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the current cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements. Subsequently, SLO-ML selects the services to honour the customer's requirements and generates the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We evaluate SLO-ML using an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess SLO-ML's overheads through empirical scalability tests. We express the promises of SLO-ML in terms of gained productivity and experienced usability, and we highlight its limitations by analysing it as application requirements grow.

U2 - 10.1145/3344341.3368805

DO - 10.1145/3344341.3368805

M3 - Conference contribution/Paper

SN - 9781450368940

SP - 241

EP - 250

BT - Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing

PB - IEEE/ACM

ER -