An advancing method for web service reliability and scalability using ResNet convolution neural network optimized with Zebra Optimization Algorithm

Abstract

Web service reliability and scalability is an important mission that keeps web services running normally. Within web service, the web services invoked by users not only depend on the service itself, but also on web load condition. Due to the features of web dynamics, traditional reliability and scalability methods have become inappropriate; at the same time, the web condition parameter sparsity problem will cause inaccurate reliability prediction. To address these challenges, Web Service Reliability and Scalability Determination Using ResNet Convolutional Neural Network optimized with Zero Optimization Algorithm (WRS-ResNetCNN-ZOA) is proposed in this manuscript. Initially, the input data is collected from WSRec dataset. The ResNet convolutional neural network (ResNetCNN) with Business Process Execution Language (BPEL) specification is introduced to forecast the reliability and scalability of web service. The results are categorized as right and wrong based on ResNetCNN. The weight parameters of the ResNetCNN is optimized by Zebra Optimization Algorithm to improve accuracy of the prediction. The performance of the proposed method is examined under some performance metrics, like F-measure, reliability, scalability, accuracy, sensitivity, specificity, and precision. The proposed technique attains 15.36%, 35.39%, 23.87%, 20.67% better reliability, 42.39%, 11.39%, 34.16%, 25.78% better accuracy when analyzed to the existing methods, like Web Reliability based on K-clustering, (WRS-KClustering), Web Reliability prediction based on AdaBoostM1 and J48 (WRS-AdaM1-J48), Web Reliability prediction based on Online service Reliability (WRS-OPUN), and Web Reliability prediction based on Dynamic Bayesian Network (WRS-DBNS), respectively.