Cost-optimized redundant data storage in the cloud

Cost-optimized redundant data storage in the cloud The use of cloud-based storage systems for storing data is a popular alternative to local storage systems. Beside several benefits of cloud-based storages, there are also downsides like vendor lock-in or unavailability. Moreover, the selection of the best fitting storage solution can be a tedious and cumbersome task and the storage requirements may change over time. In this paper, we formulate a system model that uses multiple cloud-based services to realize a redundant and cost-efficient storage. Within this system model, we formulate a local and a global optimization problem that considers historical data access information and predefined quality of service requirements to select a cost-efficient storage solution. Furthermore, we present a heuristic optimization approach for the global optimization. Extensive evaluations show the benefits of our work in comparison with a baseline that follows a state-of-the-art approach. We show that our solutions save up to 30% of the cumulative cost in comparison with the baseline. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Service Oriented Computing and Applications Springer Journals

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Publisher
Springer Journals
Copyright
Copyright © 2017 by The Author(s)
Subject
Computer Science; Computer Systems Organization and Communication Networks; Software Engineering/Programming and Operating Systems; e-Commerce/e-business; Computer Appl. in Administrative Data Processing; Management of Computing and Information Systems; IT in Business
ISSN
1863-2386
eISSN
1863-2394
D.O.I.
10.1007/s11761-017-0218-9
Publisher site
See Article on Publisher Site

Abstract

The use of cloud-based storage systems for storing data is a popular alternative to local storage systems. Beside several benefits of cloud-based storages, there are also downsides like vendor lock-in or unavailability. Moreover, the selection of the best fitting storage solution can be a tedious and cumbersome task and the storage requirements may change over time. In this paper, we formulate a system model that uses multiple cloud-based services to realize a redundant and cost-efficient storage. Within this system model, we formulate a local and a global optimization problem that considers historical data access information and predefined quality of service requirements to select a cost-efficient storage solution. Furthermore, we present a heuristic optimization approach for the global optimization. Extensive evaluations show the benefits of our work in comparison with a baseline that follows a state-of-the-art approach. We show that our solutions save up to 30% of the cumulative cost in comparison with the baseline.

Journal

Service Oriented Computing and ApplicationsSpringer Journals

Published: Sep 12, 2017

References

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