GPU enabled Improved Reference Ideal Method (I-RIM) for Web Service Selection

Web services are globally utilized by clients to accomplish the required functionality over the web. As a result of its popularity and flexibility in usage, thousands of functionally similar web services are available over the network. Hence, it becomes necessary to select the optimal web service to satisfy the clients’ need. Various methodologies like machine learning, genetic algorithm, bio-inspired techniques, multi-criteria decision making (MCDM) methods and many others aid in the process of selecting the best web service from thousands of alternatives. This paper aims in proposing a relatively new MCDM approach to solve the selection issue and thereby proposes a novel framework incorporating the proposed MCDM method to aid in the process of service selection. Reference ideal method (RIM) is a state-of-the-art MCDM technique to select the optimal web service based on user inputs. In spite of its popularity, this method is found to have multiple pitfalls which make the selection process less effective. This paper proposes a novel MCDM methodology named improved RIM (I-RIM) to overcome the existing pitfalls in RIM. The paper also proposes a novel framework which combines the power of graphics processing unit (GPU) and I-RIM to enhance the efficiency of the selection process. The proposed I-RIM when parallelized using GPU is found to outperform the parallelized MCDM techniques taken for study. The results also imply that the I-RIM is more consistent and stable towards the ranking process. It is also evident that the proposed framework which incorporates I-RIM outperforms RIM in terms of execution time, mean reciprocal rank and Spearman’s correlation coefficient which makes the framework more stable and reliable, thus, making it suitable for real-time web service selection.