Comparative study on machine learning techniques in predicting the QoS-values for web-services recommendations

Abstract

This is an era of Internet computing and computing as a service on the internet is called cloud computing. Mainly three services like SaaS (applications), PaaS, and IaaS are being accessed through internet on demand, pay as per usage basis. Quality of Service (QoS) is the main issue in internet based computing for service providers and user-dependent as well as user-independent QoS parameters. In the current work we compared different machine learning algorithms for predicting the response time and throughput QoS values using past usage data. Bagging and support vector machines are found to be better performing prediction methods in comparison with other learning algorithms.

Novel Framework for Fault Tolerant in a Cloud Environment using ACO Algorithm

“…Bagging and support vector machines (SVM) are found to be better performing prediction methods in comparison with other learning algorithms (Linear Regression, Multilayer Perceptron, Radial Basis Function Network, k-NN and Random Forest algorithms) [11]. Autonomic resource controller is proposed that dynamically controls the resource allocation for data centers virtual containers.…”