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

作     者：Nadia Tabassum Allah Ditta Tahir Alyas Sagheer Abbas Hani Alquhayz Natash Ali Mian Muhammad Adnan Khan 

作者机构：Department of Computer ScienceVirtual University of PakistanLahore54000Pakistan Department of Information SciencesDivision of Science&TechnologyUniversity of EducationLahore54000Pakistan Department of Computer ScienceLahore Garrison UniversityLahore54000Pakistan School of Computer ScienceNCBA&ELahore54000Pakistan Department of Computer Science and InformationCollege of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia School of Computer and Information TechnologyBeaconhouse National UniversityTarogilLahore53700Pakistan Faculty of ComputingRiphah School of Computing&InnovationRiphah International University Lahore CampusLahore54000Pakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第67卷第6期

页      面：3129-3141页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

主　　题：Cloud computing hyperconverged neural network QoS parameter cloud service providers ranking prediction 

摘      要：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.