Optimizing Virtual Machine Placement in Cloud Computing Environments: A Comparative Study and Novel Approach

Cloud computing has emerged as a transformative paradigm in the realm of information technology, offering scalable and on-demand access to computing resources over the internet. Effective resource management is crucial in cloud environments to ensure optimal performance, minimize energy consumption, and meet service level agreements (SLAs). This paper presents a comprehensive study of various resource optimization techniques in cloud computing, including DVFS, IQRMC, MADMU. Through extensive simulations and analysis, we evaluate the performance of these techniques in terms of resource utilization, energy efficiency, no. of VM migrations, execution time, stimulation time, and SLA adherence. Furthermore, we propose a novel VM placement algorithm, First Fit, and compare its effectiveness with existing approaches. The findings of this study provide valuable insights into the optimization of resource management in cloud computing environments and contribute to the ongoing research in this field.