InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback
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 achi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/12/4627 |
| _version_ | 1797482362487963648 |
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| author | Muhammad Munir Ud Din Nasser Alshammari Saad Awadh Alanazi Fahad Ahmad Shahid Naseem Muhammad Saleem Khan Hafiz Syed Imran Haider |
| author_facet | Muhammad Munir Ud Din Nasser Alshammari Saad Awadh Alanazi Fahad Ahmad Shahid Naseem Muhammad Saleem Khan Hafiz Syed Imran Haider |
| author_sort | Muhammad Munir Ud Din |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-09T22:31:13Z |
| format | Article |
| id | doaj.art-c184e8415faa4db9be2a6c1985179f93 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:31:13Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c184e8415faa4db9be2a6c1985179f932023-11-23T18:56:29ZengMDPI AGSensors1424-82202022-06-012212462710.3390/s22124627InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ FeedbackMuhammad Munir Ud Din0Nasser Alshammari1Saad Awadh Alanazi2Fahad Ahmad3Shahid Naseem4Muhammad Saleem Khan5Hafiz Syed Imran Haider6School of Computer Sciences, National College of Business Administration & Economics, Lahore 54700, PakistanDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi ArabiaDepartment of Basic Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi ArabiaDepartment of Information Sciences, Division of Sciences and Technology, University of Education, Lahore 54770, PakistanSchool of Computer Sciences, National College of Business Administration & Economics, Lahore 54700, PakistanDepartment of Software Engineering, University of Lahore, Lahore 54770, PakistanCloud 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).https://www.mdpi.com/1424-8220/22/12/4627cloud servicesPaaSIaaSSaaSrankingmachine learning |
| spellingShingle | Muhammad Munir Ud Din Nasser Alshammari Saad Awadh Alanazi Fahad Ahmad Shahid Naseem Muhammad Saleem Khan Hafiz Syed Imran Haider InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback Sensors cloud services PaaS IaaS SaaS ranking machine learning |
| title | InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback |
| title_full | InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback |
| title_fullStr | InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback |
| title_full_unstemmed | InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback |
| title_short | InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback |
| title_sort | intelirank a four pronged agent for the intelligent ranking of cloud services based on end users feedback |
| topic | cloud services PaaS IaaS SaaS ranking machine learning |
| url | https://www.mdpi.com/1424-8220/22/12/4627 |
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