No abstract

As per the global digital report, 52.9% of the world population is using the internet, and 42% of the world population is actively using e-commerce, banking, and other online applications. Web services are software components accessed using networked communications and provide services to end users. Software developers provide a high quality of web service. To meet the demands of user requirements, it is necessary for a developer to ensure quality architecture and quality of services. To meet the demands of user measure service quality by the ranking of web services, in this paper, we analyzed QWS dataset and found important parameters are best practices, successability, availability, response time, reliability and throughput, and compliance. We have used various data mining techniques and conducted experiments to classify QWS data set into four categorical values as class1, 2, 3, and 4. The results are compared with various techniques random forest, artificial neural network, J48 decision tree, extreme gradient boosting, K-nearest neighbor, and support vector machine. Multiple classifiers analyzed, and it was observed that the classifier technique eXtreme gradient boosting got the maximum accuracy of 98.44%, and random forest got the accuracy of 98.13%. In future, we can extend the quality of web service for mixed attributes.

No abstract

With the growth of internet-based applications and the explosion of consumers, cloud-based web service applications have become more common and the importance of minimizing the cost, increasing the interactivity, and management and efficient use of resources has become high. Existing methods like fixed cost per month no longer satisfy the application maintenance costs of the modern app developers. In this article, the authors propose an enhanced model for improving efficiency; maximize availability and minimizing the cost of cloud-based web applications. The authors have conducted experiments on grid dataset and analyzed the results using several algorithms on the load balancer with the multilevel optimized shortest remaining time scheduling method. The analysis clearly proves that applying a “pay as you” go mechanism will substantially reduce the cost and will improve the efficiency which resources are utilized. The results clearly suggest improvements in cost minimization and effective utilization of resources leading to effective utilization of services.

No abstract