A genetic PSO algorithm with QoS-aware cluster cloud service composition

Abstract

The QoS-aware cloud service composition is a significantly crucial concern in dynamic cloud environment. There is multi-nature services are clustered together and integrated with multiple domains over the internet. Because of increasing number private and public cloud sources and predominantly all cloud services offers similar services. However this differs in their functionalities depend on the QoS constraints. This drags more complexity in choosing a clustered cloud services with optimal QoS concert, an enhanced Genetic Particle Swarm Optimization (GPSO) Algorithm is anticipated to crack this crisis. With the intention to construct the QoS-aware cloud composition algorithm, all the parameters to be redefined such as price, position, response time and reputation. The Adaptive Non-Uniform Mutation (ANUM) approach is proposed to attain the best particle globally to boost the population assortment on the motivation of conquering the prematurity level of GPSO algorithm. This strategy also matched with other similar techniques to acquire the convergence intensity. The efficiency of the anticipated algorithm for QoS-aware cloud service composition is exemplified and evaluated with a Modified Genetic Algorithm (MGA), GN_S_Net, and PSOA the outcomes of investigational assessment signifies that our model extensively achieves than the existing approaches by means of execution time with improved QoS performance parameters.