Web service composition in cloud: A fuzzy rule model

Background: Cloud Computing has drawn much attention in the industry due to its cost-efficient schema along with more prospects, such as elasticity and scalability nature of Cloud Com-puting. One of the main service models of a Cloud is software as a service, where many web services are published and hosted in the Cloud environment. Many web services offered in a Cloud have similar functionality, with different of characteristics non-functional requirements such as Quality of Service (QoS). In addition, as individual web services are limited in their capability. Therefore, there is a need for composing existing services to create new functionality in the form of composite service to fulfill the requirements of Cloud user for certain processes. Methods: This paper introduces a fuzzy rule approach to compose web service based on QoS from different Cloud Computing providers. The fuzzy rule is generated based on QoS of discovered web service from Cloud in order to compose web services that only match user requirements. The proposed model is based on an agent that is responsible for discovering and composing web service that only stratified user requirements Result: the experimental result shows that the proposed model is efficient in terms of time and the use of fuzzy rules to compose web services from different Cloud providers under different specifica-tions and configurations of Cloud Computing environment. Conclusion: In this paper, an agent-based model was proposed to compose web services based on fuzzy rule in Cloud environment. The agent is responsible for discovering web services and generat-ing composition plans based on offered QoS for each web service. The agent employs a set of fuzzy rules to carry out an intelligent selection to select the best composition plan that fulfills the requirements of the end user. The model was implemented on CloudSim to ensure the validity of the proposed model and performance time analysis was performed that showed good result in terms of time with regard to the Cloud Computing configuration. © 2020 Bentham Science Publishers.