Virtual Machine Consolidation Using Enhanced Crow Search Optimization Algorithm in Cloud Computing Environment

The widespread usage of cloud computing technologies benefits the services, providers and investors in constructing the large scale data centers. However, an increase in energy consumption from Physical machines showed significant impact on environment due to emissions of carbon dioxide. The Virtual Machines (VMs) used the minimal number of Physical Machines (PMs) for performing Dynamic Consolidation obtained magic solutions for managing the power consumption. The present research uses Enhanced Crow Search Optimization Algorithm (ECSOA) technique for reducing the usage of energy by mapping the VMs migrating to hypothesis showed an aggressive consolidation. The ECSOA consolidates the VMs by migrating the number of PMs which decreases the number of hosts as they were overloaded in an environment. The number of migrations are drastically reduced as it considered VMs as a main source of migration which overloaded and under loaded the hosts. The results obtained from the research showed that the proposed ECSOA obtained SLA violation of 0.000267 better when compared to the existing Osmotic Hybrid Artificial Bee-Ant Colony Optimization (OH-BAC) and Particle Swarm Optimization (PSO)-Decimal Encoding techniques that obtained 0.5 and 0.0002 SLA violation.