Geo-distributed multi-cloud data centre storage tiering and selection with zero-suppressed binary decision diagrams

The exponential growth of data in recent years prompted cloud providers to introduce diverse geo-distributed storage solutions for various needs. The vast amount of storage options, however, presents organisations with a challenge in determining the ideal data placement configuration. The study introduces a novel optimisation algorithm utilising the zero-suppressed binary decision diagram to select the optimal data centre, storage tiers, and cloud provider. The algorithm takes on a holistic approach that considers cost, latency, and high availability, applicable to both geo-distributed on-premise environments and public cloud providers. Furthermore, the proposed methodology leverages the recursive structure of the zero-suppressed binary decision diagram, allowing for the enumeration and ranking of all valid configurations based on total cost. Overall, the study offers flexibility for organisations in addressing specific priorities for cloud storage solutions by providing alternative near-optimal configurations.

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Publication Details

Journal
International Journal of Cloud Computing
Published
2025-01-01
DOI
https://doi.org/10.1504/ijcc.2025.147441
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Geo-distributed multi-cloud data centre storage tiering and selection with zero-suppressed binary decision diagrams

Kazushi Ikeda, William Emmanuel S. Yu, Miguel de Cervantes Saavedra, Renzo Roel P. Tan et al.
International Journal of Cloud Computing
Cloud Computing and Resource Management
article

Geo-distributed multi-cloud data centre storage tiering and selection with zero-suppressed binary decision diagrams

Kazushi Ikeda, William Emmanuel S. Yu, Miguel de Cervantes Saavedra, Renzo Roel P. Tan, Brian Godwin Lim
article en

Abstract

The exponential growth of data in recent years prompted cloud providers to introduce diverse geo-distributed storage solutions for various needs. The vast amount of storage options, however, presents organisations with a challenge in determining the ideal data placement configuration. The study introduces a novel optimisation algorithm utilising the zero-suppressed binary decision diagram to select the optimal data centre, storage tiers, and cloud provider. The algorithm takes on a holistic approach that considers cost, latency, and high availability, applicable to both geo-distributed on-premise environments and public cloud providers. Furthermore, the proposed methodology leverages the recursive structure of the zero-suppressed binary decision diagram, allowing for the enumeration and ranking of all valid configurations based on total cost. Overall, the study offers flexibility for organisations in addressing specific priorities for cloud storage solutions by providing alternative near-optimal configurations.

International Journal of Cloud ComputingVol. 14(2)
Ateneo de Manila University (PH), Nara Institute of Science and Technology (JP)
Peace, Justice and strong institutions
Openalex Percentile: Top 74%
Cloud Computing and Resource Management
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Geo-distributed multi-cloud data centre storage tiering and selection with zero-suppressed binary decision diagrams — Kazushi Ikeda, William Emmanuel S. Yu, et al. · International Journal of Cloud Computing (2025) | TGRS Research Map | TGRS