IoT leads to abrupt variations producing an immense number of data streams for storage, which is a considerable task in the heterogeneous cloud computing environment. Extant techniques consider task deadlines for virtual machine (VM) allocation and migration. This creates a resource famine leading to haphazard and numerous VM migrations, high energy consumption and unbalanced resource utilization. To solve this issue, an energy-efficient resource ranking and utilization factor-based virtual machine selection (ERVS) approach is proposed. ERVS encompasses the resource requirement rate for task classification, comprehensive resource balance ranking, processing element cost and the resource utilization square model for migration. It evaluates overloaded and underloaded hosts and types of VM by predicting CPU utilization rate and energy consumption. Based on this, tasks are sorted and VMs are optimally assigned, which enhances the resource utilization rate, reducing the number of live VM migrations. The experiments evaluate the ability of the proposed approach to diminish energy consumption without violation of service level agreements. © 2018 Elsevier Ltd