17-04-2013, 03:45 PM
optimal service for pricing for cloud cache
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ABSTRACT:
Cloud applications that offer data management services are emerging. Such clouds supportcaching 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 thatmanages the service of multiple users in an efficient, but also, resource economic way thatallows 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. The model should be plausible in that it reflects the correlation of cachestructures involved in the queries. Optimal pricing is achieved based on a dynamic pricingscheme that adapts to time changes. This paper proposes a novel price-demand model designedfor 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 inan time-efficient manner. The experimental study shows the efficiency of the solution.
EXISTING SYSTEM:
Existing clouds focus on the provision of web services targeted to developers, such as AmazonElastic Compute Cloud (EC2), or the deployment of servers, such as Go Grid.There are two major challenges when trying to define an optimal pricing scheme for the cloudcaching service. The first is to define a simplified enough model of the price demanddependency, to achieve a feasible pricing solution, but not oversimplified model that is notrepresentative.A static pricing scheme cannot be optimal if the demand for services has deterministic seasonalfluctuations.
PROPOSED SYSTEM:
The cloud caching service can maximize its profit using an optimal pricing scheme. Optimal pricing necessitates an appropriately simplified price-demand model that incorporates thecorrelations of structures in the cache services. The pricing scheme should be adaptable to timechanges.
Optimal Pricing:
We assume that each structure is built from scratch in the cloud cache, as the cloud may not haveadministration rights on existing back-end structures. Nevertheless, cheap computing and parallelism on cloud infrastructure may benefit the performance of structure creation. For acolumn, the building cost is the cost of transferring it from the backendand combining it withthe currently cached columns. This cost may contain the cost of nte grating the column in theexisting cache table. For indexes, the building cost involves fetching the data across the Internetand then building the index in the cache.Since sorting is the most important step in building an index, the cost of building an index isapproximated to the cost of sorting the indexed columns. In case of multiple cloud databases, thecost of data movement is incorporated in the building cost. The maintenance cost of a column or an index is just the cost of using disk space in the cloud. Hence, building a column or an index inthe cache has a one-time static cost, whereas their maintenance yields a storage cost that is linear with time.
Price adaptivity to time changes:
Profit maximization is pursued in a finite long-term horizon. The horizon includes sequentialnon-overlapping intervals that allow for scheduling structure availability. At the beginning of each interval, the cloud redefines availability by taking offline some of the currently availablestructures and taking online some of the unavailable ones.
Pricing optimization proceeds
iterations on a sliding time-window that allows online corrections on the predicted demand, viare-injection of the real demand values at each sliding instant. Also, the iterative optimizationallows for re-definition of the parameters in the price-demand model, if the demand deviatessubstantially from the predicted.