Dynamic Virtual Machine Allocation and Load Balancing methods for Heterogeneous Task Allocation in Public Cloud

Cloud computing faces problems in conjunction with security, average overall performance monitoring, scaling, data lock-in problem, load balancing, electricity usage, beneficial useful resource scheduling and carbon emissions. In Cloud computing, the number of users has a direct relationship with the demand for resources. Therefore, in order to schedule a task a key challenge is to balance the load among resources. The volume of work in the cloud grows exponentially in proportion to the number of users. As a result, a large number of heterogeneous jobs of varying length is required to be scheduled across various heterogeneous resources. Therefore, this study proposes optimize scheduling method for efficient resource allocation and task scheduling that can assign VMs to host and distribute the task effectively.