Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

Year, pages: 2020, 51 - 82

With the advent of big data age, data volume has been changed from trillionbyte to petabyte with incredible speed. Owing to the fact that cloud storage offers the vision of a virtually infinite pool of storage resources, data can be stored and accessed with high scalability and availability. But a single cloud-based data storage has risks like vendor lock-in, privacy leakage, and unavailability. Multi-cloud storage can mitigate these risks with geographically located cloud storage providers. In this storage scheme, one important challenge is how to place a user's data cost-effectively with high availability. In this paper, an architecture for multi-cloud storage is presented. Next, a multi-objective optimization problem is defined to minimize total cost and maximize data availability simultaneously, which can be solved by an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions called the Pareto-optimal set. Then, a method is proposed which is based on the entropy method to determine the most suitable solution for users who cannot choose one from the Pareto-optimal set directly. Finally, the performance of the proposed algorithm is validated by extensive experiments based on real-world multiple cloud storage scenarios.

Wang, P., Zhao, C., Liu, W., Chen, Z., Zhang, Z. 2020. Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage. In Computing and Informatics, vol. 39, no.1-2, pp. 51-82. 1335-9150. DOI: https://doi.org/10.31577/cai_2020_1-2_51

Wang, P., Zhao, C., Liu, W., Chen, Z., Zhang, Z. (2020). Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage. Computing and Informatics, 39(1-2), 51-82. 1335-9150. DOI: https://doi.org/10.31577/cai_2020_1-2_51

Publisher: Ústav informatiky SAV