Abstract: The cloud caching service can maximize its pricing scheme based on optimization .There are two major challenges while trying to define an optimal pricing scheme in caching service. The first is to define a simplified model of the price-demand dependency and the second is to establish the pricing scheme adaptable to modelling errors, time-dependent model changes and stochastic behaviour of the application. Also, the iterative optimization allows for re-definition of the parameters in the price-demand model, hence the pricing scheme should be adaptable to time changes. In this paper we propose an optimal pricing scheme and a method for the efficient computation of structure correlation by extending a cache-based query cost estimated subroutine and template-based workload compression technique is used. Pricing optimization proceeds in iterations on a sliding time-window that allows online corrections on the predicted demand, via re-injection of the real demand values at each sliding instant and hence the cost estimation and the consistency can be maintained.
Keywords: cloud cache, optimal pricing, template based workload compression, structure correlation.