To meet the increasing demand of computational power, at present IT service providers� should choose cloud\nbased services for its flexibility, reliability and scalability. More and more datacenters are being built to cater\ncustomers� need. However, the datacenters consume large amounts of energy, and this draws negative\nattention. To address those issues, researchers propose energy efficient algorithms that can minimize energy\nconsumption while keeping the quality of service (QoS) at a satisfactory level. Virtual Machine consolidation is\none such technique to ensure energy-QoS balance. In this research, we explore fuzzy logic and heuristic\nbased virtual machine consolidation approach to achieve energy-QoS balance. A Fuzzy VM selection method\nis proposed in this research. It selects VM from an overloaded host. Additionally, we incorporate migration\ncontrol in Fuzzy VM selection method that will enhance the performance of the selection strategy. A new\noverload detection algorithm has also been proposed based on mean, median and standard deviation of\nutilization of VMs. We have used CloudSim toolkit to simulate our experiment and evaluate the performance\nof the proposed algorithm on real-world work load traces of Planet lab VMs. Simulation results demonstrate\nthat the proposed method is most energy efficient compared to others.
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