QoS-Aware Traffic-Efficient Web Service Selection over BigData Space

Service Composition is playing a pivotal role in diverse industries to approach more cost effective solutions. Web service selection is playing a key role in the domain of the composition and it considers as the most important last informant of the successful Composition. However, service selection is becoming highly resource starvation job due to the flooding of web services (volume), diverse range of Non-Functional Properties (NFP's) generation (variety) and uncertainty of NFP's (veracity). These concerns obviously open a gateway to the research directions in the BigData space. Therefore, we have proposed successful web service selection method based on BigData space while addressing internal traffic concerns during the selection process of the web services. We have employed well informed mathematical model to satisfied diverse QoS selection requirements. The novel graph theory based selection method to selects the optimal composition plan among given candidate services to choose a most profitable plan. Moreover, also, we have addressed scalability and traffic issues aroused in the Hadoop space by introducing the state of the art approaches to our method. Here we have addressed internal traffic congestions and improve the data communication of the selection process. These approaches are working effectively towards efficient service selection. Our experiment results show that our approach allows to produces more scalable and effective selection result.