Providing an Improved Resource Management Approach for Healthcare Big Data Processing in Cloud Computing Environment

• Published in: International journal of advanced computer science & applications Vol. 14; no. 7
• Main Authors: Zhou, Fei, Ding, Huaibao, Ding, Xiaomei
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2023
• Subjects: Algorithms; Big Data; Cloud computing; Data processing; Forecasting techniques; Health care; Health care industry; Machine learning; Metadata; Resource management; Virtual environments
• ISSN: 2158-107X; 2156-5570; 2156-5570
• DOI: 10.14569/IJACSA.2023.0140782

Due to the gathering of big data and the advancement of machine learning, the healthcare industry has recently experienced fast change. Acceleration of operations related to the analysis and retrieval of healthcare data is essential to facilitate surveillance. However, providing healthcare to the community is a complex task that is highly dependent on data processing. Also, processing health metadata can be very expensive for organizations. To meet the strict service quality requirements of the healthcare industry, large-scale healthcare data processing in the cloud confederation has emerged as a viable option. However, there are many challenges, including optimal resource management for metadata processing. Based on this, in the present study, a fuzzy solution for determining the optimal cloud using the resource forecasting technique is presented for health big data processing. During job processing, a fuzzy selection-based VM migration technique was used to move a virtual machine (VM) from a high-load server to a low-load server. The proposed architecture is divided into regional and global levels. After evaluating the local component, requests are sent to the global component. If the local component cannot meet the requirements, the request is sent to the global component. The hierarchical structure of the proposed method requires the generation of delivered requests before estimating the available resources. The proposed solution is compared with PSO and ACO algorithms according to different criteria. The simulation results show the effectiveness and efficiency of the model compared to alternative methods.