Envisioning SLO-driven service selection in multi-cloud applications

Elhabbash, Abdessalam; Elkhatib, Yehia; Blair, Gordon S.; Lin, Yuhui; Barker, Adam; Thomson, John

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Date

02/12/2019

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Abstract

The current large selection of cloud instances that are functionally equivalent makes selecting the right cloud service a challenging decision. We envision a model driven engineering (MDE) approach to raise the level of abstraction for cloud service selection. One way to achieve this is through a domain specific language (DSL) for modelling the service level objectives (SLOs) and a brokerage system that utilises the SLO model to select services. However, this demands an understanding of the provider SLAs and the capabilities of the current cloud modelling languages (CMLs). This paper investigates the state-of-the-art for SLO support in both cloud providers SLAs and CMLs in order to identify the gaps for SLO support. We then outline research directions towards achieving the MDE-based cloud brokerage.

Citation

Elhabbash , A , Elkhatib , Y , Blair , G S , Lin , Y , Barker , A & Thomson , J 2019 , Envisioning SLO-driven service selection in multi-cloud applications . in UCC 2019 Companion - Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing . Association for Computing Machinery, Inc , pp. 9-14 , 12th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2019 , Auckland , New Zealand , 2/12/19 . https://doi.org/10.1145/3368235.3368831

conference

Publication

UCC 2019 Companion - Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing

DOI

https://doi.org/10.1145/3368235.3368831

Type

Conference item

Rights

Copyright © 2019 Association for Computing Machinery. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1145/3368235.3368831

Description

Funding: Adaptive Brokerage for the Cloud (ABC) project, UK EPSRC grants EP/R010889/1 and EP/R010528/1.

Collections

University of St Andrews Research

URL

https://eprints.lancs.ac.uk/id/eprint/138561/

URI

http://hdl.handle.net/10023/19334