Analytics-as-a-Service in a Multi-Cloud Environment through Semantically enabled Hierarchical Data Processing

TL;DR

This paper proposes a hierarchical, semantically-enabled data processing architecture for multi-cloud IoT analytics, demonstrating its scalability and feasibility through a practical implementation across multiple cloud platforms.

Contribution

It introduces an innovative architecture supporting heterogeneous multi-cloud environments with semantic integration, validated by a real-world system implementation.

Findings

System is scalable and efficient

No significant overheads observed

Supports heterogeneous multi-cloud IoT analytics

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 integration, 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.