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Analytics-as-a-Service in a Multi-Cloud Environment through Semantically-enabled Hierarchical Data Processing
Jayaraman, P.P. Perera, C. Georgakopoulos, D. Dustdar, S. Thakker, Dhaval Ranjan, R.
Jayaraman, P.P.
Perera, C.
Georgakopoulos, D.
Dustdar, S.
Thakker, Dhaval
Ranjan, R.
Publication Date
2017-08
End of Embargo
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© 2016 Wiley Periodicals, Inc. Full-text reproduced in accordance with the publisher’s self-archiving policy. This is the peer reviewed version of the following article: Jayaramani PP, Perera C, Georgakopoulos D, Dustdar S, Thakker D and Ranjan R (2016) Analytics-as-a-Service in a Multi-Cloud Environment through Semantically-enabled Hierarchical Data Processing. Software: Practice and Experience. 47(8): 1139-1156, which has been published in final form at http://dx.doi.org/10.1002/spe.2432. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
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2016-07-03
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Abstract
A large number of cloud middleware platforms and tools are deployed to support a variety of Internet
of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used
by its owners to achieve their primary and predefined objectives, where raw and processed data are only
consumed by them. However, allowing third parties to access processed data to achieve their own objectives
significantly increases intergation, cooperation, and can also lead to innovative use of the data. Multicloud,
privacy-aware environments facilitate such data access, allowing different parties to share processed
data to reduce computation resource consumption collectively. However, there are interoperability issues in
such environments that involve heterogeneous data and analytics-as-a-service providers. There is a lack of
both - architectural blueprints that can support such diverse, multi-cloud environments, and corresponding
empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative
hierarchical data processing architecture that utilises semantics at all the levels of IoT stack in multicloud
environments. We demonstrate the feasibility of such architecture by building a system based on this
architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments.
The evaluation shows that the system is scalable and has no significant limitations or overheads.
Version
Accepted manuscript
Citation
Jayaraman PP, Perera C, Georgakopoulos D, Dustdar S, Thakker D and Ranjan R (2017) Analytics-as-a-Service in a Multi-Cloud Environment through Semantically-enabled Hierarchical Data Processing. Software: Practice and Experience. 47(8): 1139-1156.
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Article