Hybrid Fog and Cloud Integration: Balancing Performance and Energy Consumption Using Machine Learning Techniques
D. Balakrishnan, Umasree Mariappan, S. Hariharasitaraman, Nilamadhab Mishra · 2025
A viable strategy to deal with the difficulties in contemporary computer settings is the integration of fog and cloud computing. To balance performance and energy usage, this study investigates the possibilities of hybrid fog and cloud integration. This study introduces a new VM scheduling method that uses historical data on VM resource use to improve traditional methods for scheduling cloud VMs. The method improves where virtual machines are placed by ensuring they are set up on the best physical machines, using performance data from K-nearest neighbors (KNN) and Naive Bayes classification. Optimizing actual CPU consumption also resolves resource conflict between virtual machines. Results from experiments show how well this system works to increase overall performance and resource efficiency by streamlining conventional instance-based physical machine selection techniques. By lowering the number of physical machines needed for deployment, the suggested method gradually adjusts to system dynamics.