An Algorithm for Finding the Minimum Cost of Storing and Regenerating Datasets in Multiple Clouds - 2018


The proliferation of cloud computing allows users to flexibly store, re-compute or transfer large generated datasets with multiple cloud service providers. However, because of the pay-as-you-go model, the overall value of using cloud services depends on the consumption of storage, computation and bandwidth resources that are 3 key factors for the value of IaaS-based mostly cloud resources. So as to scale back the entire value for knowledge, given cloud service suppliers with completely different pricing models on their resources, users will flexibly opt for a cloud service to store a generated dataset, or delete it and choose a cloud service to regenerate it whenever reused. But, finding the minimum value could be a complicated yet unsolved drawback. In this Project, we tend to propose a novel algorithm that may calculate the minimum value for storing and regenerating datasets in clouds, i.e., whether datasets should be stored or deleted, and furthermore where to store or to regenerate whenever they are reused. This minimum price also achieves the best trade-off among computation, storage and bandwidth prices in multiple clouds. Comprehensive analysis and rigid theorems guarantee the theoretical soundness of the paper, and general (random) simulations conducted with fashionable cloud service providers' pricing models demonstrate the excellent performance of our approach.

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