Effective Load Balancing on Servers Using Virtual Machines for ‎Cloud Computing Environment Using Green Computing and The Skewness Algorithm

Efficient resource management in cloud data centers is crucial for optimizing resource utilization while maintaining performance and energy ‎efficiency. This study presents the design and implementation of an automated dynamic resource management system that balances overload ‎avoidance and green computing. Overload avoidance is achieved by dynamically adjusting physical machine (PM) resources to meet virtual ‎machine (VM) demands, preventing performance degradation due to excessive resource contention. Simultaneously, energy efficiency is ‎ensured by minimizing the number of active PMs, turning off unused ones to conserve power. To enhance resource allocation, the concept ‎of skewness is introduced to measure the uneven utilization of server resources. By reducing skewness, overall server efficiency is ‎improved while addressing multi-dimensional resource constraints. The proposed system is implemented and evaluated using the ‎CLOUDSIM 2.0 simulation platform with NetBeans, demonstrating its effectiveness in optimizing resource allocation, preventing overload, ‎and reducing energy consumption in cloud environments.