An Efficient Resource Allocation for Processing Healthcare Data in the Cloud Computing Environment

Nowadays, processing large-media healthcare data in the cloud has become an effective way of satisfying the medical users' QoS (quality of service) demands. Providing healthcare for the community is a complex activity that relies heavily on information processing. Such processing can be very costly for organizations. However, processing healthcare data in cloud has become an effective solution to meet QoS demands of health users. In this paper, a fuzzy-based solution is presented for determining the optimal cloud using resource prediction technique. Besides, to make balance during the processing of tasks, based on fuzzy selector the virtual machine (VM) migration technique is used to migrate a VM from an overload server to an underload one. The proposed framework consists of two parts, local and global. To deliver the application to the global part, the local part must first be checked. If it is not suitable, the request will be delivered to the global part; indeed, the proposed framework works in a hierarchical manner. At first, a list of received requests is created and then using the proposed solution, the amount of available resources is estimated based on which the requested resources are allocated for processing. We used the Cloudsim toolkit to evaluate the proposed solution under various parameters and results have been compared with those of FAHP and ICA-K-Means algorithms. Compared to FAHP, the simulation results show that the proposed solution benefits from a 10% cost reduction and and a 12% reduction in cost compared to ICA-K-Means. Moreover, compared to FAHP and ICA-K-Means, the proposed method enjoys a reduction in number of rejected requests and an increase of 8% and 7% performance compared to the FAHP and ICA-K-Means, respectively.