Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects

Optimal fitness aware cloud service composition using modified invasive weed optimization

Quality of Service (QoS)-aware cloud service composition is one of the pivotal problems in cloud computing. With the seamless proliferation of cloud services, it becomes challenging to obtain an optimal cloud service for composition that satisfies a user's requirements. Many composition models available in the literature compose cloud services based on one or two QoS parameters of the candidate services without considering the complete set. These composition models do not consider the connectivity constraints between the candidate cloud services for satisfying a workflow/function in a service composition. In this paper, we present a novel Optimal Fitness Aware Cloud Service Composition using Modified Invasive Weed Optimization dealing with multiple QoS parameters and satisfying the balancing of QoS parameters and the connectivity constraints of cloud service composition. We evaluate the performance of our approach on a data set of real world cloud services, to select the best optimal fitness aware cloud service composition. By performing the parametric and non-parametric test at 1% level of significance, our proposed method is statistically more accurate than the other methods compared