Standardized Big Data Processing in Hybrid Clouds

Ingo Simonis

Abstract

There is a growing number of easily accessible Big Data repositories hosted on cloud infrastructures that offer additional sets of cloud-based products such as compute, storage, database, or analytics services. The Sentinel-2 earth observation satellite data available via Amazon S3 is a good example of a petabyte-sized data repository in a rich cloud environment. The combination of hosted data and co-located cloud services is a key enabler for efficient Big Data processing. When the transport of large amounts of data is not feasible or cost efficient, processes need to be shipped and executed as closely as possible to the actual data. This paper describes standardization efforts to build an architecture featuring high levels of interoperability for provisioning, registration, deployment, and execution of arbitrary applications in cloud environments. Based on virtualization mechanisms and containerization technology, the standardized approach allows to pack any type of application or multi-application based workflow into a container that can be dynamically deployed on any type of cloud environment. Consumers can discover these containers, provide the necessary parameterization and execute them online even easier than on their local machines, because no software installation, data download, or complex configuration is necessary.

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Paper Citation


in Harvard Style

Simonis I. (2018). Standardized Big Data Processing in Hybrid Clouds.In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-294-3, pages 205-210. DOI: 10.5220/0006681102050210


in Bibtex Style

@conference{gistam18,
author={Ingo Simonis},
title={Standardized Big Data Processing in Hybrid Clouds},
booktitle={Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2018},
pages={205-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006681102050210},
isbn={978-989-758-294-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Standardized Big Data Processing in Hybrid Clouds
SN - 978-989-758-294-3
AU - Simonis I.
PY - 2018
SP - 205
EP - 210
DO - 10.5220/0006681102050210