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Prediction of Cloud Ranking in a hyperconverged Cloud Ecosystem Using Machine Learning

Computers, Materials & Continua 2021年第6期67卷 3129-3141页

作者： Nadia Tabassum Allah Ditta Tahir Alyas Sagheer Abbas Hani Alquhayz Natash Ali Mian Muhammad Adnan Khan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Computer Science NCBA&ELahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia School of Computer and Information Technology Beaconhouse National UniversityTarogilLahore53700Pakistan Faculty of Computing Riphah School of Computing&InnovationRiphah International University Lahore CampusLahore54000Pakistan

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the *** design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter *** a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging *** selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud *** emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this *** software-based unified utilities combine storage virtualization,compute virtualization,and network *** availability of the latter has also raised the demand for *** to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful *** is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various *** research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.

关键词： Cloud computing hyperconverged neural network QoS parameter cloud service providers ranking prediction

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