Dynamic Provision of Workload Allocation on Cloud Resources Using Machine Learning Techniques

Cloud tasks are scheduled to the respective VM for execution. Scheduling process selects the resources and allocates the requesting task to them. The resources are selected in advance and then the request is forwarded for processing of tasks. It suffers the overestimation problem which means that the requested task size is less than the allocated resource level. Various scheduling problems are addressed but still the problem arises during the resource allocation. Auto scaling process is implemented in the cloud which allocates the resource based on the demand. The proposed model uses the dynamic method of cloud allocation and provisioning based on the workload level and resource level. Priority queue is implemented prior to the provisioning process for ordering the resource in specific order. The main aim of the proposed method is to allocate the resources using dynamic priority manner. The efficiency is achieved at maximum level by selecting and allocating the workload.