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.
Highlights
The healthcare sector is perceiving a huge growth fueled by a growing population and results in the focused wellness to the consumers
A 2014 report from consulting corporation EMC and research firm IDC place the volume of global healthcare figures at 153 exabytes in 2013
In another approach (Arabnejad et al, 2017) authors have proposed a fuzzy rule-based scheme, where they have come up with two methods that is Fuzzy Q Learning and Fuzzy SARSA Learning; used for scaling down/ scaling up the Cloud resources as per the requirement of Quality of Services (QoS) and used to reduce the cloud cost by refining the cloud resource consumption. (Yan et al, 2016) presents an approach where reinforcement learning (RL) based approach has been used for dynamic decision making for resource utilization based self-management technique
Summary
The healthcare sector is perceiving a huge growth fueled by a growing population and results in the focused wellness to the consumers. This is due to the information technologies-enabled patient care systems. The organizations are migrating to the cloud-based healthcare services to strengthen their expectation to meet with the demand. We have adopted the mechanism of reinforcement learning (RL) to train the agent for the cloud environment. The established training data including output and input will be provided Based on these data the model will be trained to predict the output from an unobserved input. The rest of this paper is organized as; the section describes the background and related work, motivation and contribution have been described, followed by Model used for communication, results and discussions, research challenges, and ended with conclusion and future work
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of E-Health and Medical Communications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
