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Monitoring IaaS Cloud for Healthcare Systems: Healthcare Information Management and Cloud Resources Utilization

Monitoring IaaS Cloud for Healthcare Systems: Healthcare Information Management and Cloud Resources Utilization

Vivek Kumar Prasad, Madhuri D. Bhavsar
Copyright: © 2020 |Volume: 11 |Issue: 3 |Pages: 17
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799806912|DOI: 10.4018/IJEHMC.2020070104
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MLA

Prasad, Vivek Kumar, and Madhuri D. Bhavsar. "Monitoring IaaS Cloud for Healthcare Systems: Healthcare Information Management and Cloud Resources Utilization." IJEHMC vol.11, no.3 2020: pp.54-70. http://doi.org/10.4018/IJEHMC.2020070104

APA

Prasad, V. K. & Bhavsar, M. D. (2020). Monitoring IaaS Cloud for Healthcare Systems: Healthcare Information Management and Cloud Resources Utilization. International Journal of E-Health and Medical Communications (IJEHMC), 11(3), 54-70. http://doi.org/10.4018/IJEHMC.2020070104

Chicago

Prasad, Vivek Kumar, and Madhuri D. Bhavsar. "Monitoring IaaS Cloud for Healthcare Systems: Healthcare Information Management and Cloud Resources Utilization," International Journal of E-Health and Medical Communications (IJEHMC) 11, no.3: 54-70. http://doi.org/10.4018/IJEHMC.2020070104

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Abstract

Healthcare functionality is enriched by cloud services which offers a perspective for broad integration and interoperability. Cloud-based facilities support healthcare systems to remain connected to remote access devices to various tasks and information. The healthcare actors should have an understanding of the risks and benefits associated with the usage of Cloud Computing resources utilization. Also, they must launch an appropriate contract-based relationship between the Cloud Service Providers and the actors of healthcare systems by means of Service Level Agreements (SLAs). The variation in both demand and supply within the healthcare information affects the use of information technology. Hence, monitoring resources can play an important role in accommodating the healthcare data. To deal with the aforementioned problems; reinforcement learning mechanisms along with the metrics has been used and experimented with the various dynamics of workload to deliver services with quality assurance.