Storage Virtualization for Hybrid Cloud: A Policy-Driven Object Storage Layer with Placement, Compliance, and Cost Constraints
DOI:
https://doi.org/10.56830/IJSIE202615Keywords:
Storage virtualization, Hybrid cloud object storage, Policy-driven placement, S3-compatible storage, Data residency complianceAbstract
The study proposes a policy-based S3-compatible virtualization layer that can be extended to both private MinIO/Ceph and public object storage services. It also analyzes five representative traces (T15, T29, T65, T78, and T79), containing about 17.4 million requests, within a larger set of about 1.6 billion requests that represent 342 million unique objects from a collection of traced IBM Cloud Object Storage requests. The 5 reported modeled-cost comparisons have a low of 38.7% modeled cost reduction compared to static tiering, a high of 47.2%, and an unweighted mean of 43.7%. These numbers refer to differences in the modeled totals, and do not represent statistically significant differences or newly confirmed cloud bills. Geography or regulatory classifications are not recorded with the dataset, and assumptions about compliance and causation of egress are made during simulation but not included in the dataset. HMAC-SHA-256 is used to validate the authenticity of policy metadata, rather than physical residence. Allows for repeatable assessments such as measurable tariff snapshots, baseline definition, fault-injection tests, and more.
References
Azumah, K. K., Maciel, P. R., Sørensen, L. T., & S. Kosta. (2023). “Modeling and simulating a process mining-influenced load-balancer for the hybrid cloud,”. IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 1999–2010, Apr.–Jun. https://ieeexplore.ieee.org/abstract/document/9781255/.
Baron, B., Bousquet, A., Metens, E., Pimpale, S., Puz, N., Sauveur, M. d., et al. (2026). “ACOS: Apple’s geo-distributed object store at exabyte scale,” in. Proc. 24th USENIX Conf. File and Storage Technologies (FAST ’26), Santa Clara, CA, USA, pp. 53–66. https://www.usenix.org/system/files/fast26-baron-updated.pdf.
Bonthu, C., Malik, G., & M. R. Dhanagari. (2025). “AI-driven secure and scalable data architecture with multi-domain MDM and cybersecurity for real-time fintech decisionmaking,” in. Proc. 2025 Tenth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), pp. 1–7. IEEE. https://ieeexplore.ieee.org/document/11374416.
Brambilla, M., Falconi, M., Fortina, V. M., Plebani, P., & M. Vitali. (2025). “Automating policy-as-code generation pipeline for data products: An OpenAPI-driven Rego generator,”. in Service-Oriented Computing: 23rd International Conference, ICSOC https://re.public.polimi.it/retrieve/6c5801d5-c25d-4965-8dbd- 9ed966b705ec/ICSOC25_Policy_Short.pdf.
Carrizales-Espinoza, D., Sanchez-Gallegos, D. D., Gonzalez-Compean, J. L., & J. Carretero. (2024). “StructMesh: A storage framework for serverless computing continuum,”. Future Generation Computer Systems, vol. 159, pp. 353–369, Oct. 2024. https://www.sciencedirect.com/science/article/pii/S0167739X24002401 .
Dockara, T. R., & M. Malhotra. (2026). “TrustDS: Policy-compiled governance and verifiable evidence for cross-cloud marketplace analytics under explicit security assumptions,”. Scientific Reports, vol. 16, Art. no. 17958, https://www.nature.com/articles/s41598- 026-48740-8_reference.pdf.
E. Ofer. (2021). “Evaluating Cache Performance Using Cloud Storage Traces,”. Storage Developer Conference EMEA, Storage Networking Industry Association (SNIA), June, https://www.snia.org/educational-library/evaluating-cache-performance-using-cloudstorage-traces-2021.
Eytan, O., Harnik, D., Ofer, E., Friedman, R., & R. Kat. (2020). “It’s Time to Revisit LRU vs. FIFO,” in . Proc. 12th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage ’20), https://www.usenix.org/conference/hotstorage20/presentation/eytan.
Flinn, J., Dou, X., Aggarwal, A., Boyko, A., Richard, F., Sun, E., et al. (2022). “Owl: Scale and flexibility in distribution of hot content,” in. Proc. 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22), pp. 1–15. https://www.usenix.org/system/files/osdi22-flinn.pdf.
Gefen, I., Zuck, A., Bransky, D., Hershcovitch, M., Harnik, D., & D. Tsafrir. (2025). “Why paying for storage beats free networking in cloud bursting,” in. Proc. 17th ACM Workshop on Hot Topics in Storage and File Systems, Jul, pp. 31–37. https://dl.acm.org/doi/pdf/10.1145/3736548.3737826.
Goggin, Z., Lekshmanan, A., Valverde, R., & E. Bocchi. (2025). “Ceph at CERN in the multi-datacentre era,” . EPJ Web of Conferences, , vol. 337, Art. no. 01331. https://www.epjconferences.org/articles/epjconf/pdf/2025/22/epjconf_chep2025_01331.pdf.
Hardt, A., Nahhas, A., Müller, H., & K. Turowski. (2025). “Deep reinforcement learning for selecting the optimal hybrid cloud placement combination of standard enterprise IT applications,” in. Proc. International Conference on Enterprise Information Systems (ICEIS), vol. pp. 434–443. https://www.scitepress.org/Papers/2025/132108/132108.pdf.
J. Demšar. (2006). “Statistical Comparisons of Classifiers over Multiple Data Sets,” . Journal of Machine Learning Research, vol. 7, pp. 1–30. https://www.jmlr.org/beta/papers/v7/demsar06a.html.
Juliussen, B. A., Kozyri, E., Johansen, D., & J. P. Rui. (2023). “The third country problem under the GDPR: Enhancing protection of data transfers with technology,”. International Data Privacy Law, vol. 13, no. 3, pp. 225–243, Aug. 2023. https://doi.org/10.1093/idpl/ipad013.
Ke, Q., Lv, Y., Zhang, Z., Shen, Z., Yu, Y., Chen, H., et al. (2026). “CrossFS: Improving cross-domain file system performance with CRDT-based metadata synchronization,”. ACM Transactions on Storage,, vol. 22, no. 2, pp. 1–25, https://dl.acm.org/doi/pdf/10.1145/3777470.
Khan, A. Q., Matskin, M., Prodan, R., Bussler, C., Roman, D., & A. Soylu. (2024). “Cloud storage cost: A taxonomy and survey,” . World Wide Web, vol. 27, no. 4, Art. no. 36, 2024. https://link.springer.com/content/pdf/10.1007/s11280-024-01273-4.pdf.
Khan, A. Q.; Matskin, M.; Prodan, R.; Bussler, C.; Roman, D.; A. Soylu. (2024). “Cloud storage tier optimization through storage object classification,”. Computing,, vol. 106, pp. 3389–3418. https://link.springer.com/content/pdf/10.1007/s00607-024-01281- 2.pdf.
Liu, S., Mo, X., Hershcovitch, M., Zhang, H., Cheng, A., Girmonsky, G., et al. (2025). “SkyStore: Cost-optimized object storage across regions and clouds,” . arXiv preprint arXiv, 2502.20818, https://arxiv.org/pdf/2502.20818.
N. M. K. Koneru. (2025). “AI-driven optimization of CI/CD pipelines for multi-cloud infrastructure deployment,” in. Proc. 2025 Tenth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Nov, pp. 1–6. IEEE. https://ieeexplore.ieee.org/abstract/document/11374441/.
Nogueira, L., & A. Resende. (2026). “Reusing policy-as-code across CI/CD and Kubernetes admission control: An empirical assessment of governance consistency,” . Computers , vol. 15, no. 7, Art. no. 453. https://www.mdpi.com/2073-431X/15/7/453.
Pallewatta, S., & M. A. Babar. (2024). “Towards secure management of edge-cloud IoT microservices using policy as code,”. in European Conference on Software Architecture, Cham, Switzerland: Springer Nature Switzerland, Sep., pp. 270–287. https://arxiv.org/pdf/2406.18813.
Pang, L., Alazzawe, A., Ray, M., Kant, K., & J. Swift. (2023). “Adaptive intelligent tiering for modern storage systems,” . Performance Evaluation, vol. 160, Art. no. 102332. https://www.sciencedirect.com/science/article/am/pii/S0166531623000020.
Park, H., Qiu, Z., Ganger, G. R., & Amvrosiadis, G. (2024). “Reducing cross-cloud/region costs with the auto-configuring Macaron cache,” in. Proc. ACM SIGOPS 30th Symposium on Operating Systems Principles, pp. 347–368. https://dl.acm.org/doi/pdf/10.1145/3694715.3695972.
Poat, M. D., Lauret, J., & T. Rao. (2024). “Federated access from DOE labs to distributed storage in the EIC era of computing,” in. EPJ Web of Conferences, vol. 295, p. 04021, May https://www.epjconferences.org/articles/epjconf/pdf/2024/05/epjconf_chep2024_04021.pdf.
Rangu, S., Chadha, K. S., & S. Samala. (2025). “AI-powered cloud architecture, agile analytics, and enterprise DevOps-enabled framework for healthcare and insurance ecosystem optimisation,” in. Proc. 2025 Tenth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), pp. 1–7. IEEE. https://ieeexplore.ieee.org/document/11374414.
Ristov, S., Brandacher, S., Hautz, M., Felderer, M., & R. Breu. (2024). “CODE: Code once, deploy everywhere serverless functions in federated FaaS,”. Future Generation Computer Systems, vol. 160, pp. 442–456, 2024. https://www.sciencedirect.com/science/article/pii/S0167739X24003170.
Sadavarte, M. V., Reddy, A. K., Moghe, D., Kannan, F. S., Moorjani, S., Kamat, C. D., et al. U.S.
Patent . 12,596,682. https://patents.google.com/patent/US12596682B2/en.
Sardana, J., Hariharan, R., & R. S. Pudi. (2025). “AI-driven secure and resilient healthcare data platforms with intelligent search and personalized communication,”. in Proc. 2025 Tenth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Nov. , pp. 1–6. IEEE. https://ieeexplore.ieee.org/document/11374370.
Venkatesh, R. T., Chandrashekar, D. K., Rao, P. B., Sridhar, R., & S. Rajanna. (2023). “Systematic approaches to data placement, replication and migration in heterogeneous edge-cloud computing systems: A comprehensive literature review,”. Ingenierie des Systemes, vol. 28, no. 3, p. 751, 2023. https://openurl.ebsco.com/contentitem/gcd:167351562?sid=ebsco%3Aplink%3Acrawl er-gcd&id=ebsco%3Agcd%3A167351562&crl=c&jrnl=16331311.
Xing, S., & Y. Wang. (2025). “Proactive data placement in heterogeneous storage systems via predictive multi-objective reinforcement learning,” . IEEE Access, vol. 13, pp. 117986–117998, https://ieeexplore.ieee.org/iel8/6287639/6514899/11072103.pdf.
Zhu, Y., Dhakal, A., Bruel, P., Rattihalli, G., Xiao, Y., Lombardi, J., et al. (2025). “An RDMA-first object storage system with SmartNIC offload,” in. Proc. SC'25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1650–1657. https://dl.acm.org/doi/pdf/10.1145/3731599.3767522.
