Affiliation:
1. Assistant professor, Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu India
2. Associate Professor, Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur Andhra Pradesh India
3. Senior Assistant Professor, Department of Computer Science and Engineering SASTRA Deemed to be University, Srinivasa Ramanujan Center Kumbakonam, Thanjavur Tamil Nadu India
4. Professor, Department of Information Technology Sri Sairam Institute of Technology Sai Leo Nagar, Chennai Tamil Nadu India
Abstract
AbstractWeb service composition (WSC), a distributed architecture, creates new services atop existing ones. Ensuring trust and assessing performance and dependability in online services coordination is essential. In this paper, “Web Service Reliability and Scalability Determination Using Depth Wise Separable Convolutional Neural Network” (WSRS‐DWSCNN) is proposed to assess the trustworthiness of online service compositions, particularly focusing on performance and dependability. This work addresses the need to predict the reliability and scalability of Business Process Execution Language (BPEL) composite web services. The proposed approach transforms the BPEL specification into a Depth Wise Separable Convolutional Neural Network (DWSCNN) and annotates it with probabilistic properties for prediction. The DWSCNN model classifies the outcomes as correct or incorrect, and to enhances the prediction of web service composition scalability and reliability, we optimize the DWSCNN's weight parameters using the Adolescent Identity Search Algorithm (AISA). The proposed technique is activated in Python and its efficacy is analyzed under some metrics, such as reliability, scalability, accuracy, sensitivity, specificity, precision, F‐measure. The proposed method provides 12.36%, 45.39%, and 25.97% better reliability, 41.39%, 11.39%, 34.16% better accuracy compared with existing methods like, Web service reliability prediction depending on machine learning (WSRS‐K‐means), reliability prediction method for multiple state cloud/edge‐basis network utilizing deep neural network (WSRS‐DNN‐BO), and improving reliability of mobile social cloud computing utilizing machine learning in content addressable network (WSRS‐CAN), respectively.