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Dynamic consolidation of virtual machines (VMs) is an effective way to improve the utilization of resources and energy efficiency in grid computing. We propose a novel load balancing approach that combines the Group Method of Data Handling (GMDH) based on Geneticalgorithmfor host overload prediction and the Minimum Migration Time (MMT) policy for VM selection. The GA-GMDH algorithm could predict the actual host load in each consecutive future time interval. We evaluate our method using the workload traces of Google Cluster data. Our proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the Service Level Agreements (SLAs). Keywords-grid computing;virtualization;dynamic consolidation; host overload prediction; group method; minimum migration time; energy efficiency

GMDH based on Genetic Algorithm and MMT Policy for Energy Efficient Dynamic Consolidation of Virtual Machines