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We developed a metamodel to define the structure and content of our cloud cost model.
First, a cloud cost model is obtained for each cloud provider/platform considered as a potential migration target.
As our approach requires users to populate a cloud cost model from data on a provider's website, we considered ease-of-use to be essential.
We require a cloud cost model, a source of workload data, and a schema, which could also be obtained from NoSQL databases [17, 18].
These costs are defined in a cloud cost model for the target cloud platform, which is also input into the simulation.
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Automating the extraction of cloud cost models would reduce error and time, and prevent lock-in [69, 70], so another area of future work is the creation of a model extraction approach and tool for our cloud cost models.
Our plans for future work include: (1) the use of additional parameters when simulating database capacity, (2) extending the approach to support relational-to-NoSQL migrations, and (3) automating the extraction of cloud cost models.
It uses the rules and transformations to generate a set of valid partitionings over the available clouds, and then applies a cost model to rank them.
Although there are some efforts to define cost models for clouds, the need for a comprehensive cost model, which covers all cost factors and types of clouds, is undeniable.
When computing the cost, MigSim rounds the migration duration up to the nearest full hour, in line with the cost model of many cloud providers.
Based on the proposed cost model in [49], the mobile cloud service cost function could be written as follows: cloud service costs = ∑ j = 1 J α comp ( γ j + η j ) + α j UL R j + α j DL λ j ′ (12).
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Justyna Jupowicz-Kozak
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