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
Accepted author manuscript, 3.64 MB, PDF document
Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -