Optimization of Uncertainty in Cloud Resource Provisioning with Security

Abstract

The cloud computing provides the resources to the cloud consumers via the internet. The cloud provider provides two types of provisioning for accessing resources such as Advance reservation and on-demand plans to the cloud users. On-demand users should pay as pay-per-use basis. On-demand users should pay more cost than advance reservation plan. A stochastic programming model which considers a set of scenarios such as price, demand, storage, and dynamic allocation is used for the resource provisioning for reservation plans in cloud computing. The uncertainty problem deals with a large set of scenarios for the real time problems. A huge set of scenarios consideration leads to more consumption of time and computational complexity. A scenario reduction technique, particle swarm optimization (PSO) is applied to reduce the scenarios and provides a lesser set of scenarios. Security can be provided while consumer send data to the cloud provider by using advanced encryption standard (AES) algorithm.