Optimizing Cloud Computing Resources: An Energy Efficient Multi-QoS Factor-Based VM Placement Strategy

Abstract

Cloud computing is experiencing unprecedented demand, offering scalable and flexible resources for a wide range of applications. However, this surge in demand has raised concerns about energy consumption and the need for environmentally sustainable solutions. Green computing has emerged as a critical consideration in this context. Virtual Machine Placement (VMP) is a key component of optimizing cloud resources, aiming to allocate virtual machines efficiently while minimizing energy consumption, cost, and load balancing. This paper addresses the VMP problem by introducing a novel approach based on multifactor optimization, specifically the Diversity Constraint Digger Snake Optimizer (DCD-SO). It offers an innovative perspective on optimizing virtual machine placement by considering energy efficiency, load balancing, and resource utilization simultaneously with the aim to reduce VM migration count, time and cost. Proposed method provides a more comprehensive and sustainable solution, aligning with the principles of green computing. Through extensive simulations and experiments, we have rigorously evaluated the performance of DCD-SO in comparison to traditional optimization techniques such as Particle Swarm Optimization (PSO) and Snake Optimization. In our analysis of actual cloud environments, we compared the results of our method with existing state-of-the-art techniques. Result outcomes determine showed that proposed approach has reduced migration count of 5 and 3 for scheduling 42VMs and 84VMs on 16 and 32 host units respectively than traditional MOGANS, GA-S, GA-N and GA-NN methods. This comprehensive evaluation reinforces the effectiveness and practicality of our approach in addressing the intricate challenges of Virtual Machine Placement (VMP) in dynamic cloud computing settings. As cloud computing continues to evolve, our study contributes to more sustainable and efficient resource management, addressing both current demands and future needs.