Publication: Towards Cloud Storage Tier Optimization...
Master data
Title: | Towards Cloud Storage Tier Optimization with Rule-Based Classification |
Subtitle: | |
Abstract: | Cloud storage adoption has increased over the years as more and more data has been produced with particularly high demand for fast processing and low latency. To meet the users’ demands and to provide a cost-effective solution, cloud service providers (CSPs) have offered tiered storage; however, keeping the data in one tier is not a cost-effective approach. Hence, several two-tiered approaches have been developed to classify storage objects into the most suitable tier. In this respect, this paper explores a rule-based classification approach to optimize cloud storage cost by migrating data between different storage tiers. Instead of two, four distinct storage tiers are considered, including premium, hot, cold, and archive. The viability and potential of the approach are demonstrated by comparing cost savings achieved when data was moved between tiers versus when it remained static. The results indicate that the proposed approach has the potential to significantly reduce cloud storage cost, thereby providing valuable insights for organizations seeking to optimize their cloud storage strategies. Finally, the limitations of the proposed approach are discussed along with the potential directions for future work, particularly the use of game theory to incorporate a feedback loop to extend and improve the proposed approach accordingly. |
Keywords: | Storage tiers, cloud, optimization, StaaS, cloud storage |
Publication type: | Article in compilation (Authorship) |
Publication date: | 2023 (Print) |
Published by: |
ESOCC 2023 Proceedings of the European Conference on Service-Oriented and Cloud Computing
ESOCC 2023 Proceedings of the European Conference on Service-Oriented and Cloud Computing
(
Springer, Cham;
)
to publication |
Title of the series: | Lecture Notes in Computer Science |
Volume number: | 14183 |
First publication: | Yes |
Version: | - |
Page: | pp. 205 - 216 |
Versionen
Keine Version vorhanden |
Publication date: | 2023 |
ISBN: |
|
ISSN: | 0302-9743 |
Homepage: | https://link.springer.com/chapter/10.1007/978-3-031-46235-1_13 |
Publication date: | 12.10.2023 |
ISBN (e-book): |
|
eISSN: | 1611-3349 |
DOI: | http://dx.doi.org/10.1007/978-3-031-46235-1_13 |
Homepage: | https://link.springer.com/chapter/10.1007/978-3-031-46235-1_13 |
Open access |
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Authors
Akif Quddus Khan (external) |
Nikolay Nikolov (external) |
Mihhail Matskin (external) |
Radu Aurel Prodan (internal) |
Christoph Bussler (external) |
Dumitru Roman (external) |
Ahmet Soylu (external) |
Assignment
Organisation | Address | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
Categorisation
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Research Cluster | No research Research Cluster selected |
Peer reviewed |
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Publication focus |
Classification raster of the assigned organisational units:
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working groups |
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Cooperations
Organisation | Address | ||||
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Norwegian University of Science and Technology
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NO
- 7491
Trondheim |
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SINTEF Digital
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NO
Oslo |
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Royal Institute of Technology
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SE
Stockholm |
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Robert Bosch LLC
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US - 94085 Sunnyvale, CA 94085 |
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Oslo Metropolitan University
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NO - 0130 Oslo |
Research activities
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Publications: | No related publications |
Events: | No related events |
Lectures: | No related lectures |