Key points are not available for this paper at this time.
This paper aims to present and deploy an improved task scheduling algorithm for the allocation of user tasks across multiple computing resources.The primary goal of this algorithm is to minimize both execution time and costs while simultaneously enhancing resource utilization within the context of medical applications.Virtual machine scheduling in a heterogeneous cloud environment needs significant attention with the increase in the usage of cloud resources by end users and enterprises.It is one of the significant parameters that affects cloud data centers.The resources requested by every user vary in their configuration.Finding a suitable virtual machine for each process is dynamically a time-consuming process.Virtual machines are classified based on resources such as memory and processing units.Upon the arrival of a request with specific requirements, it can be effortlessly mapped to a corresponding virtual machine.This process is followed by a bilateral method encompassing queuing and scheduling.Queues are formed for requests with different requirements, which are followed by a scheduling algorithm that allocates VMs based on the minimum remaining resources in the resource pool.A scheduling mechanism has been designed to solve the problem of starvation that occurs with the Min-Min fit scheduling policy.The average turnaround time and waiting times are observed to be significantly reduced, which has an impact on the performance of the data center for medical applications.Using the CloudSim Plus tool, the experimental outcomes demonstrated that the proposed approach exhibited remarkable superiority over competing methods in relation to metrics such as average waiting time, turnaround time, and response time.This advantage was observed when compared to multiple algorithms that were examined during the study.
Ganapriya et al. (Tue,) studied this question.