MELODIC: Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing

MELODIC: Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing

01.12.2016 – 31.01.2020

Big Data is one of the major current trends in ICT. In areas as diverse as social media, business intelligence, information security, Internet-of-Things, and scientific research, a tremendous amount of, possibly unstructured, data is created or collected at a speed surpassing what we can handle using traditional techniques. Several cloud providers offer services to support data-intensive cloud applications, typically implemented by large-scale data processing frameworks such as Apache Spark and Apache Hadoop. The lack of data-aware cloud federation, however, keeps current cloud computing from realising the full potential of data-intensive applications in the cloud.

The vision of the MELODIC project is to enable federated cloud computing for data-intensive applications, and provide the user with an easy-to-use unified cloud environment, hiding the complexity of a multi-cloud. The MELODIC platform will enable big data and data-intensive applications by transparently taking advantage of distinct characteristics of available private and public clouds, dynamically optimise resource utilisation, consider data locality, conform to the user’s privacy needs and service requirements, and counter vendor lock-in. From the perspective of the user, the MELODIC framework will appear as an infrastructure-agnostic middleware platform supporting development, deployment, and execution of data-intensive applications on distributed and heterogeneous multi-clouds.

The MELODIC platform will be built as an integration of three open source platforms available from three running European projects (PaaSage, CACTOS, and PaaSword), supplemented with the two main open source big data frameworks Hadoop and Spark.

Final goal

If successful, the MELODIC platform will enable data-intensive applications to run within defined security, cost, and performance boundaries seamlessly on geographically distributed and federated cloud infrastructures.

Funding source

This project is funded by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 731664. EU Horizon 2020 (ICT-06-2016 - Cloud Computing)

All partners

Project leader

Geir Horn, University of Oslo

Media presence

MELODIC website

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