A Multi-criteria Approach for Large-object Cloud Storage

Uwe Hohenstein, Michael C. Jaeger, Spyridon V. Gogouvitis

Abstract

In the area of storage, various services and products are available from several providers. Each product possesses particular advantages of its own. For example, some systems are offered as cloud services, while others can be installed on premises, some store redundantly to achieve high reliability while others are less reliable but cheaper. In order to benefit from the offerings at a broader scale, e.g., to use specific features in some cases while trying to reduce costs in others, a federation is beneficial to use several storage tools with their individual virtues in parallel in applications. The major task of a federation in this context is to handle the heterogeneity of involved systems. This work focuses on storing large objects, i.e., storage systems for videos, database archives, virtual machine images etc. A metadata-based approach is proposed that uses the metadata associated with objects and containers as a fundamental concept to set up and manage a federation and to control storage locations. The overall goal is to relieve applications from the burden to find appropriate storage systems. Here a multi-criteria approach comes into play. We show how to extend the object storage developed by the VISION Cloud project to support federation of various storage systems in the discussed sense.

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Paper Citation


in Harvard Style

Hohenstein U., Jaeger M. and Gogouvitis S. (2017). A Multi-criteria Approach for Large-object Cloud Storage . In Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-255-4, pages 75-86. DOI: 10.5220/0006432100750086


in Bibtex Style

@conference{data17,
author={Uwe Hohenstein and Michael C. Jaeger and Spyridon V. Gogouvitis},
title={A Multi-criteria Approach for Large-object Cloud Storage},
booktitle={Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2017},
pages={75-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006432100750086},
isbn={978-989-758-255-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - A Multi-criteria Approach for Large-object Cloud Storage
SN - 978-989-758-255-4
AU - Hohenstein U.
AU - Jaeger M.
AU - Gogouvitis S.
PY - 2017
SP - 75
EP - 86
DO - 10.5220/0006432100750086