Discovery of ranking fraud for Mobile Towards Optimized Fine-Grained Pricing of IaaS Cloud Platform
Mrs. S.JYOTHIREDDY, Mr. G.LAKPATHI, , ,
In Cloud many pricing schemes in IaaS platform are already proposed with pay-as-you-go and subscription/spot market policy to guarantee service level agreement, it is still inevitable to suffer from wasteful payment because of coarse grained pricing scheme. In this paper, we investigate an optimized fine-grained and fair pricing scheme. Two tough issues are addressed: (1) the profits of resource providers and customers often contradict mutually; (2) VM-maintenance overhead like startup cost is often too huge to be neglected. Not only can we derive an optimal price in the acceptable price range that satisfies both customers and providers simultaneously, but we also find a best-fit billing cycle to maximize social welfare (i.e., the sum of the cost reductions for all customers and the revenue gained by the provider). We carefully evaluate the proposed optimized fine-grained pricing scheme with two large-scale real-world production traces (one from Grid Workload Archive and the other from Google data center). We compare the new scheme to classic coarse-grained hourly pricing scheme in experiments and find that customers and providers can both benefit from our new approach. The maximum social welfare can be increased up to 72:98% and 48:15% with respect to DAS-2 trace and Google trace respectively.
S.JYOTHIREDDY et al., International Journal of Computer Engineering In Research Trends
Volume 3, Issue 05, May-2016, pp. 350-355
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