A High Performance Random Forest Machine Learning Algorithm for QoS- Based Web Services Composition

Abstract

Service-based architecture becomes a key software interface for complex applications which can be dynamically and flexibly compounded by integrating existing Web services with standard protocols from various providers. New web services can adversely affect the quality of service and customer satisfaction through their rapid introduction into a competitive business environment. Hence, it remains a constant problem in state of the art research how to collect, aggregate and employ data on individual quality of service (QoS) components to obtain the optimum quality of service of a composite service that responds to customer needs. This study proposes a Random Forest (RF) Algorithm for high-performance machine learning web services composition (WSC) problem. In this experiment, the validation is done using value iteration, iterative policy evaluation, and policy iteration algorithm. In this experiment, we illustrate WSC difficulties how to solve and demanding an entire sequence of 1000,000 Web services can be measured using an Intel Core i7 device with a 32GB RAM, requiring the selection of 10,000 services from the current system and less than 130 seconds. Besides, only seven individual web services pose a real WSC problem that needs processing power of less than 0.03 seconds.