Risk-Aware Cloud Storage Placement for Enterprise Analytics: Balancing Cost, Latency, Recoverability, and Emissions
Abstract
Cloud storage placement decisions often optimise cost and latency while treating recoverability and environmental impact as separate concerns. We developed a risk-aware placement model that jointly considered storage price, access latency, recovery requirements, regional failure exposure, and electricity carbon intensity. The model was evaluated on 480 enterprise analytics workload profiles across candidate regions and storage tiers. Multiobjective optimisation produced a diverse set of feasible placements rather than a single universally optimal region. Compared with cost-minimising placement, risk-aware solutions reduced expected recovery exposure by 37% with a median cost increase of 6%. Carbon-aware constraints reduced estimated operational emissions by a further 14% but occasionally increased latency for interactive workloads. The greatest trade-offs occurred when low-carbon regions were geographically distant from users or lacked an independent recovery location. Sensitivity analysis showed that business downtime cost strongly altered the preferred balance between price and redundancy. Storage-placement decisions are therefore better represented as transparent trade-offs among performance, resilience, cost, and emissions, with hard service constraints applied before secondary optimisation objectives.
References
1. Nazir M. Performance comparison of AWS EBS, EFS and S3 for enterprise cloud-storage workloads. International Journal of Business & Computational Sciences. 2022;2(1). Available from: https://ijbcs.org/index.php/IJBCS/article/view/2022-01-05
2. Jahid A, Haque GMM, Ansari I, Azam MA. Sustainable IT infrastructure and green data analytics: measuring environmental performance in digital enterprises. The American Journal of Engineering and Technology. 2026;8(3):80-106. doi:10.37547/tajet/Volume08Issue03-06.
3. Masanet E, Shehabi A, Lei N, Smith S, Koomey J. Recalibrating global data center energy-use estimates. Science. 2020;367(6481):984-986. doi:10.1126/science.aba3758.
4. Barroso LA, Hölzle U. The case for energy-proportional computing. Computer. 2007;40(12):33-37. doi:10.1109/MC.2007.443.
5. Beloglazov A, Abawajy J, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst. 2012;28(5):755-768. doi:10.1016/j.future.2011.04.017.
6. Dayarathna M, Wen Y, Fan R. Data center energy consumption modeling: a survey. IEEE Commun Surv Tutor. 2016;18(1):732-794. doi:10.1109/COMST.2015.2481183.
7. Henderson P, Hu J, Romoff J, Brunskill E, Jurafsky D, Pineau J. Towards the systematic reporting of the energy and carbon footprints of machine learning. J Mach Learn Res. 2020;21(248):1-43. Available from: https://jmlr.org/papers/v21/20-312.html
8. Dodge J, Prewitt T, Tachet des Combes R, Odmark E, Schwartz R, Strubell E, et al. Measuring the carbon intensity of AI in cloud instances. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 2022. doi:10.1145/3531146.3533234.
9. Radovanović A, Koningstein R, Schneider I, Chen B, Duarte A, Roy B, et al. Carbon-aware computing for datacenters. arXiv [Preprint]. 2021. doi:10.48550/arXiv.2106.11750.
10. World Resources Institute, World Business Council for Sustainable Development. The greenhouse gas protocol: a corporate accounting and reporting standard. Revised edition. Washington (DC): World Resources Institute. 2004. Available from: https://ghgprotocol.org/corporate-standard
11. Green Software Foundation. Software Carbon Intensity specification [Internet]. [cited 2026 Sep 22]. Available from: https://sci.greensoftware.foundation/
12. Freitag C, Berners-Lee M, Widdicks K, Knowles B, Blair GS, Friday A. The real climate and transformative impact of ICT: a critique of estimates, trends, and regulations. Patterns. 2021;2(9):100340. doi:10.1016/j.patter.2021.100340.
13. Lakens D. Sample size justification. Collabra Psychol. 2022;8(1):33267. doi:10.1525/collabra.33267.
14. Nosek BA, Ebersole CR, DeHaven AC, Mellor DT. The preregistration revolution. Proc Natl Acad Sci U S A. 2018;115(11):2600-2606. doi:10.1073/pnas.1708274114.
15. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. doi:10.1038/sdata.2016.18.
16. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013;4:863. doi:10.3389/fpsyg.2013.00863.
17. Efron B. Bootstrap methods: another look at the jackknife. Ann Stat. 1979;7(1):1-26. doi:10.1214/aos/1176344552.
18. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995;57(1):289-300. doi:10.1111/j.2517-6161.1995.tb02031.x.
Published
Issue
Section
License
Authors retain copyright. Articles published under the Creative Commons Attribution 4.0 International licence (CC BY 4.0) may be shared and adapted for any purpose, including commercially, provided appropriate credit is given, a link to the licence is supplied, and changes are indicated. No additional legal or technological restrictions may be imposed. Third-party material is included only where its credit line permits. Licence: https://creativecommons.org/licenses/by/4.0/. Earlier publications remain subject to their stated licence and author agreements unless the rights holder authorizes a change.
