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  • 标题:Effective Fault prediction using classifier analysis for cloud environment
  • 本地全文:下载
  • 作者:Deepak K Gaur ; Ajitabh Mahalkari
  • 期刊名称:International Journal of Computer Science and Information Technologies
  • 电子版ISSN:0975-9646
  • 出版年度:2021
  • 卷号:12
  • 期号:2
  • 页码:42-47
  • 语种:English
  • 出版社:TechScience Publications
  • 摘要:Cloud computing had opened a new horizon for utilization of resources and their computing. Emerging model of cloud computing to manage distributed large scale infrastructure resources is becoming very popular. Entities in both industry and individuals are investing a huge time and effort to investigate and develop its features. Fault tolerance is the ability to handle the faults and remove failures in such a way that the system will perform its functions unaware that any methods have been taken to handle the faults. Cloud Computing promises real costs effective and agility to organizations. Cloud computing serves the demands of a number of individuals and organizations. The cloud environment consists of virtual machines. Virtual machines utilize the infrastructure capacity of resources. The performance of virtual machines monitors can be monitored in terms of response time, throughput, and bandwidth. The proposed model utilizes these performance statistics to detect the faults after that fault is recognized by data analytics and machine learning algorithms. Fault prediction can be helpful to treat faults well in advance and appropriate action can be taken to make the system more reliable. We also developed a prototypes for data collection and training using .net and python libraries. Statistical results found satidfactory at the intial stages of research and experiments.
  • 关键词:Cloud Computing;Fault Tolerance;prediction;statistics;analysis
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