Optimal Service Pricing for Cloud Based Services
Deepak Mishra1, Manish Shrivastava2

1Deepak Mishra, Information Technology, RGPV, LNCT, LNCT, Bhopal (M.P.), India,
2Dr. Manish Shrivastava, Information Technology, RGPV, LNCT, LNCT, Bhopal (M.P.), India
Manuscript received on April 04, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 531-342 | Volume-3, Issue-2, May 2013. | Retrieval Number: C0847062312/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Cloud computing is a profound revolution in the way it offers the computation capability. The main objective now is to reduce the cost of deploying a service in the cloud and having proper coordinative in between models. Public, private, and hybrid cloud environments all face the performance limitations inherent in today’s applications and networks. In order for enterprises to maximize the flexibility and cost savings of the Public, private, and hybrid cloud they must overcome the same latency and bandwidth constraints that challenge distributed IT infrastructure environments. By overcoming application and network performance problems, Cloud Steelhead accelerates the process of migrating data and applications to the cloud, and accelerates access to that data from anywhere Cloud Computing applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in a time-efficient manner and also applied some prediction technique in between correlation models with the use of cooperative cache from self as well as different hybrid cloud.
Keywords: Cloud data management, data services, cloud service pricing, Cooperative Cache, Prediction Technique.