Optimized Energy Efficient Resource Management in Cloud Datacenters using GreenCloud Sim
The current invention outlines a Java-driven framework for efficient resource management in cloud data centres using the CloudSim simulation environment. The framework presents a predictive auto-scaling mechanism that examines historical traffic patterns to forecast future workload requirements, allowing for predictive Virtual Machine (VM) al- location rather than traditional fixed threshold-based techniques. Prior to VM migration, the system assesses a Service Level Agreement (SLA) risk factor to avoid performance degradation and potential SLA violations through intelligent power management. A specific Green Scheduler Algorithm dynamically consolidates Virtual Machines by allocating workloads to optimally loaded physical machines based on fore- casted workload conditions. Machines with low utilization are automatically migrated across different power-saving states, such as idle, sleep, and deep sleep modes. This comprehensive framework strikes a balance between energy savings and the preservation of service reliability and Quality of Service (QoS). Simulation results demonstrate the effectiveness of energy savings, improved resource utilization, and SLA compliance, making it suitable for scalable and ecofriendly cloud resource management.