InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

Sensors

Fahad Ahmad¹  Saad Awadh Alanazi²  Nasser Alshammari²  Shahid Naseem³  Hafiz Syed Imran Haider⁴  Muhammad Saleem Khan⁵  Muhammad Munir Ud Din⁵ 

[1] Department of Basic Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi Arabia;Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi Arabia;Department of Information Sciences, Division of Sciences and Technology, University of Education, Lahore 54770, Pakistan;Department of Software Engineering, University of Lahore, Lahore 54770, Pakistan;School of Computer Sciences, National College of Business Administration & Economics, Lahore 54700, Pakistan;

关键词: cloud services;    PaaS;    IaaS;    SaaS;    ranking;    machine learning;   

DOI  :  10.3390/s22124627

来源: DOAJ

【 摘 要 】

Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0–6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).

【 授权许可】

Unknown