In the age of big data, analyzing healthcare data in the cloud through efficient resource management is recognized as an effective solution to meet the quality-of-service needs of medical users. In this regard, this paper proposes a fuzzy selector-based approach to improve the virtual machine (VM) migration process that provides a balance between servers during the processing of tasks. The proposed approach is a hierarchical resource prediction that includes local and global parts for processing requests. Evaluations demonstrate the superiority of the proposed approach over state-of-the-art methods. The results show that the proposed approach reduces the cost by 10% compared to FAHP.
ghobaei-arani, M., & Akhavan, H. (2023). Towards Cloud-based Resource Management for Healthcare Data Analysis. Soft Computing Journal, (), -. doi: 10.22052/scj.2023.246269.1059
ghobaei-arani, M., Akhavan, H. (2023). 'Towards Cloud-based Resource Management for Healthcare Data Analysis', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2023.246269.1059
VANCOUVER
ghobaei-arani, M., Akhavan, H. Towards Cloud-based Resource Management for Healthcare Data Analysis. Soft Computing Journal, 2023; (): -. doi: 10.22052/scj.2023.246269.1059
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