Predictive Method for Service Composition in Heterogeneous Environments within Client Requirements

Abstract

Cloud computing is a new delivery model for Information Technology services. Many actors and parameters play an important role in provisioning of dynamically elastic and virtualized resources at the levels of infrastructures, platforms, and softwares. Now-a-days, many cloud services are competing and present often similar offers. From the customer side, it is not always easy to select a suitable service according to customer requirements and cloud services scoring. In a real-world scenario, this is more complicated since service scoring may change over time. Besides this, it interferes with many parameters such as hardware, network infrastructure, customer demand, etc. To tackle this issue, this research work presents a novel approach for the prediction of the future score of any service in order to satisfy user requirements when executing service composition in cloud environments. This approach deals with regression techniques in order to predict the expected future offer of service based on sampling service's history as well as user expectations.