IoT leads to abrupt variations resulting in an immense number of data streams for storage, which is a major task in the heterogeneous cloud computing environment. Extant techniques consider allocation and migration task deadlines for virtual machine (VM). This Energy-Efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT creates a resource famine that leads to haphazard and numerous VM migrations, high energy consumption, and unbalanced resource utilization. An energy-efficient resource ranking and factor-based virtual machine selection (ERVS) approach is proposed to solve this issue.

Energy-Efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT ERVS encompasses the resource requirement rate for task classification, comprehensive resource balance ranking, processing element cost and resource use square model for migration. By predicting CPU utilization rate and energy consumption, it evaluates overloaded and underloaded hosts and types of VM.

Conclusion

The author presents a taxonomy of energy-efficient cloud computing techniques in this paper. Various algorithms were studied and their findings and improved parameters are listed in the table. This paper can help readers find the merits and limitations present in the literature of the proposed energy-efficient algorithms.