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A Taxonomy for Cloud Storage Cost

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Management of Digital EcoSystems (MEDES 2023)

Abstract

The cost of using cloud storage services is complex and often an unclear structure, while it is one of the important factors for organisations adopting cloud storage. Furthermore, organisations take advantage of multi-cloud or hybrid solutions to combine multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, optimise cost, etc. This complicated ecosystem makes it even harder to understand and manage cost. Therefore, in this paper, we provide a taxonomy of cloud storage cost in order to provide a better understanding and insights on this complex problem domain.

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Notes

  1. 1.

    https://aws.amazon.com/s3/pricing/.

  2. 2.

    https://cloud.google.com/storage/pricing.

  3. 3.

    https://azure.microsoft.com/en-us/pricing/details/storage/blobs/.

  4. 4.

    https://aws.amazon.com/s3/faqs/.

  5. 5.

    https://docs.microsoft.com/en-us/azure/storage/blobs/storage-redundancy.

  6. 6.

    https://cloud.google.com/storage/docs/redundancy.

References

  1. Ali, M., Bilal, K., Khan, S.U., et al.: DROPS: division and replication of data in cloud for optimal performance and security. IEEE Trans. Cloud Comput. 6(2), 303–315 (2018). https://doi.org/10.1109/TCC.2015.2400460

    Article  Google Scholar 

  2. Balaji, S., Krishnan, M.N., Vajha, M., Ramkumar, V., et al.: Erasure coding for distributed storage: an overview. Sci. China Inf. Sci. 61(10), 1–45 (2018). https://doi.org/10.1007/s11432-018-9482-6

    Article  Google Scholar 

  3. Barika, M., Garg, S., Zomaya, A.Y., et al.: Orchestrating big data analysis workflows in the cloud: research challenges, survey, and future directions. ACM Comput. Surv. 52(5) (2019). https://doi.org/10.1145/3332301

  4. Edwin, E.B., Umamaheswari, P., Thanka, M.R.: An efficient and improved multi-objective optimized replication management with dynamic and cost aware strategies in cloud computing data center. Clust. Comput. 22(5), 11119–11128 (2019). https://doi.org/10.1007/s10586-017-1313-6

    Article  Google Scholar 

  5. Erradi, A., Mansouri, Y.: Online cost optimization algorithms for tiered cloud storage services. J. Syst. Softw. 160, 110457 (2020). https://doi.org/10.1016/j.jss.2019.110457

    Article  Google Scholar 

  6. Georgios, C., Evangelia, F., Christos, M., Maria, N.: Exploring cost-efficient bundling in a multi-cloud environment. Simul. Model. Pract. Theory 111, 102338 (2021). https://doi.org/10.1016/j.simpat.2021.102338

    Article  Google Scholar 

  7. Gessert, F., Wingerath, W., Friedrich, S., Ritter, N.: NoSQL database systems: a survey and decision guidance. Comput. Sci. Res. Dev. 32(3–4), 353–365 (2017). https://doi.org/10.1007/s00450-016-0334-3

    Article  Google Scholar 

  8. Hong, J., Dreibholz, T., Schenkel, J.A., Hu, J.A.: An overview of multi-cloud computing. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds.) WAINA 2019. AISC, vol. 927, pp. 1055–1068. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15035-8_103

    Chapter  Google Scholar 

  9. Hossain, K., Roy, S.: A data compression and storage optimization framework for IoT sensor data in cloud storage. In: Proceedings of the 21st International Conference of Computer and Information Technology (ICCIT 2018), pp. 1–6. IEEE (2018). https://doi.org/10.1109/ICCITECHN.2018.8631929

  10. Irie, R., Murata, S., Hsu, Y.F., Matsuoka, M.: A novel automated tiered storage architecture for achieving both cost saving and QoE. In: Proceedings of the 8th International Symposium on Cloud and Service Computing (SC2 2018), pp. 32–40. IEEE (2018). https://doi.org/10.1109/SC2.2018.00012

  11. Jin, H., Wu, C., Xie, X., Li, J., et al.: Approximate code: a cost-effective erasure coding framework for tiered video storage in cloud systems. In: Proceedings of the 48th International Conference on Parallel Processing (ICPP 2019), pp. 1–10. ACM (2019). https://doi.org/10.1145/3337821.3337869

  12. Krumm, N., Hoffman, N.: Practical estimation of cloud storage costs for clinical genomic data. Pract. Lab. Med. 21, e00168 (2020). https://doi.org/10.1016/j.plabm.2020.e00168

    Article  Google Scholar 

  13. Lee, C., Murata, S., Ishigaki, K., Date, S.: A data analytics pipeline for smart healthcare applications. In: Resch, M.M., Bez, W., Focht, E., Gienger, M., Kobayashi, H. (eds.) Sustained Simulation Performance 2017, pp. 181–192. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66896-3_12

    Chapter  Google Scholar 

  14. Li, J., Li, B.: Erasure coding for cloud storage systems: a survey. Tsinghua Sci. Technol. 18(3), 259–272 (2013)

    Article  Google Scholar 

  15. Li, W., Yang, Y., Yuan, D.: A novel cost-effective dynamic data replication strategy for reliability in cloud data centres. In: Proceedings of the 9th International Conference on Dependable, Autonomic and Secure Computing, pp. 496–502. IEEE (2011). https://doi.org/10.1109/DASC.2011.95

  16. Liu, G., Shen, H.: Minimum-cost cloud storage service across multiple cloud providers. IEEE/ACM Trans. Netw. 25(4), 2498–2513 (2017). https://doi.org/10.1109/ICDCS.2016.36

    Article  Google Scholar 

  17. Liu, J., Shen, H., Narman, H.S.: Popularity-aware multi-failure resilient and cost-effective replication for high data durability in cloud storage. IEEE Trans. Parallel Distrib. Syst. 30(10), 2355–2369 (2018). https://doi.org/10.1109/TPDS.2018.2873384

    Article  Google Scholar 

  18. Liu, M., Pan, L., Liu, S.: To transfer or not: an online cost optimization algorithm for using two-tier storage-as-a-service clouds. IEEE Access 7, 94263–94275 (2019). https://doi.org/10.1109/ACCESS.2019.2928844

    Article  Google Scholar 

  19. Liu, M., Pan, L., Liu, S.: Keep hot or go cold: a randomized online migration algorithm for cost optimization in STaaS clouds. IEEE Trans. Netw. Serv. Manage. 18(4), 4563–4575 (2021). https://doi.org/10.1109/TNSM.2021.3096533

    Article  Google Scholar 

  20. Liu, M., Pan, L., Liu, S.: Effeclouds: a cost-effective cloud-of-clouds framework for two-tier storage. Futur. Gener. Comput. Syst. 129, 33–49 (2022). https://doi.org/10.1016/j.future.2021.11.012

    Article  Google Scholar 

  21. Expedient LLC: System Redundancy in Cloud Computing. https://www.expedienttechnology.com/blog/cloud/system-redundancy-in-cloud-computing/

  22. Mansouri, N., Javidi, M.: A new prefetching-aware data replication to decrease access latency in cloud environment. J. Syst. Softw. 144, 197–215 (2018). https://doi.org/10.1016/j.jss.2018.05.027

    Article  Google Scholar 

  23. Mansouri, Y., Buyya, R.: To move or not to move: cost optimization in a dual cloud-based storage architecture. J. Netw. Comput. Appl. 75, 223–235 (2016). https://doi.org/10.1016/j.jnca.2016.08.029

    Article  Google Scholar 

  24. Mansouri, Y., Erradi, A.: Cost optimization algorithms for hot and cool tiers cloud storage services. In: Proceedings of the 11th International Conference on Cloud Computing (CLOUD 2018), pp. 622–629. IEEE (2018). https://doi.org/10.1109/CLOUD.2018.00086

  25. Mansouri, Y., Toosi, A.N., Buyya, R.: Cost optimization for dynamic replication and migration of data in cloud data centers. IEEE Trans. Cloud Comput. 7(3), 705–718 (2017). https://doi.org/10.1109/TCC.2017.2659728

    Article  Google Scholar 

  26. Mansouri, Y., Toosi, A.N., Buyya, R.: Data storage management in cloud environments: taxonomy, survey, and future directions. ACM Comput. Surv. 50(6), 1–51 (2017). https://doi.org/10.1145/3136623

    Article  Google Scholar 

  27. Mazumdar, S., Seybold, D., Kritikos, K., Verginadis, Y.: A survey on data storage and placement methodologies for cloud-big data ecosystem. J. Big Data 6, 15 (2019). https://doi.org/10.1186/s40537-019-0178-3

    Article  Google Scholar 

  28. Melo, R., Sobrinho, V., Feliciano, F., Maciel, P., et al.: Redundancy mechanisms applied to improve the performance in cloud computing environments. J. Adv. Theor. Appl. Inform. 4(1), 45–51 (2018)

    Article  Google Scholar 

  29. Mokadem, R., Hameurlain, A.: A data replication strategy with tenant performance and provider economic profit guarantees in cloud data centers. J. Syst. Softw. 159, 110447 (2020). https://doi.org/10.1016/j.jss.2019.110447

    Article  Google Scholar 

  30. Naldi, M., Mastroeni, L.: Cloud storage pricing: a comparison of current practices. In: Proceedings of the 2013 International Workshop on Hot Topics in Cloud Services (HotTopiCS 2013), pp. 27–34. ACM (2013). https://doi.org/10.1145/2462307.2462315

  31. Nalebuff, B.: Bundling as an entry barrier. Q. J. Econ. 119(1), 159–187 (2004)

    Article  MathSciNet  Google Scholar 

  32. Nannai John, S., Mirnalinee, T.: A novel dynamic data replication strategy to improve access efficiency of cloud storage. Inf. Syst. e-Bus. Manage. 18(3), 405–426 (2020). https://doi.org/10.1007/s10257-019-00422-x

    Article  Google Scholar 

  33. Nguyen, S., Salcic, Z., Zhang, X., Bisht, A.: A low-cost two-tier fog computing testbed for streaming IoT-based applications. IEEE Internet Things J. 8(8), 6928–6939 (2020). https://doi.org/10.1109/JIOT.2020.3036352

    Article  Google Scholar 

  34. Nuseibeh, H.: Adoption of cloud computing in organizations. In: AMCIS 2011 Proceedings - All Submissions, p. 372 (2011)

    Google Scholar 

  35. Oh, K., Qin, N., Chandra, A., Weissman, J.: Wiera: policy-driven multi-tiered geo-distributed cloud storage system. IEEE Trans. Parallel Distrib. Syst. 31(2), 294–305 (2019). https://doi.org/10.1109/TPDS.2019.2935727

    Article  Google Scholar 

  36. Priya, N., Punithavathy, E.: A review on database and transaction models in different cloud application architectures. In: Shakya, S., Du, K.L., Haoxiang, W. (eds.) Proceedings of Second International Conference on Sustainable Expert Systems. Lecture Notes in Networks and Systems, vol. 351, pp. 809–822. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-7657-4_65

    Chapter  Google Scholar 

  37. Ramamurthy, A., Saurabh, S., Gharote, M., Lodha, S.: Selection of cloud service providers for hosting web applications in a multi-cloud environment. In: Proceedings of the International Conference on Services Computing (SCC 2020), pp. 202–209. IEEE (2020). https://doi.org/10.1109/SCC49832.2020.00034

  38. Shah, A., Banakar, V., Shastri, S., Wasserman, M., et al.: Analyzing the impact of GDPR on storage systems. In: Proceedings of the 11th USENIX Conference on Hot Topics in Storage and File Systems. USENIX Association (2019)

    Google Scholar 

  39. Simon, H., Wuebker, G.: Bundling-a powerful method to better exploit profit potential. In: Fuerderer, R., Herrmann, A., Wuebker, G. (eds.) Optimal Bundling, pp. 7–28. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-662-09119-7_2

    Chapter  Google Scholar 

  40. Tomarchio, O., Calcaterra, D., Modica, G.D.: Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks. J. Cloud Comput. 9(1), 49 (2020). https://doi.org/10.1186/s13677-020-00194-7

    Article  Google Scholar 

  41. Tos, U., Mokadem, R., Hameurlain, A., Ayav, T., et al.: Ensuring performance and provider profit through data replication in cloud systems. Clust. Comput. 21(3), 1479–1492 (2018). https://doi.org/10.1007/s10586-017-1507-y

    Article  Google Scholar 

  42. Waibel, P., Matt, J., Hochreiner, C., et al.: Cost-optimized redundant data storage in the cloud. Serv. Orient. Comput. Appl. 11(4), 411–426 (2017). https://doi.org/10.1007/s11761-017-0218-9

    Article  Google Scholar 

  43. Wu, C., Buyya, R., Ramamohanarao, K.: Cloud pricing models: taxonomy, survey, and interdisciplinary challenges. ACM Comput. Surv. 52(6) (2019). https://doi.org/10.1145/3342103

  44. Zhang, Y., Ghosh, A., Aggarwal, V., Lan, T.: Tiered cloud storage via two-stage, latency-aware bidding. IEEE Trans. Netw. Serv. Manage. 16(1), 176–191 (2018). https://doi.org/10.1109/TNSM.2018.2875475

    Article  Google Scholar 

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Acknowledgments

This research is partially funded by DataCloud project (EU H2020 101016835).

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Correspondence to Ahmet Soylu .

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Khan, A.Q. et al. (2024). A Taxonomy for Cloud Storage Cost. In: Chbeir, R., Benslimane, D., Zervakis, M., Manolopoulos, Y., Ngyuen, N.T., Tekli, J. (eds) Management of Digital EcoSystems. MEDES 2023. Communications in Computer and Information Science, vol 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-51643-6_23

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  • DOI: https://doi.org/10.1007/978-3-031-51643-6_23

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