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Task scheduling and energy efficiency seem to be the necessary design requirements for current computing systems in recent years. It extends from single servers to data centers and clouds, as they consume large amounts of electrical power. For this reason, an effective energy management for cloud data centers is essential. At present, many researchers have focused and implemented biologically-based calculations as a desirable paradigm for addressing heterogeneity and the growth of energy crisis with skill and no added complications. Similarly, for our work, we selected biological behavior of Korean insects and chosen FFO-based migration method. The benchmark for choosing it is the rapid convergence and global optimization. In addition, the notion of limiting the overall increase in power increases with respect to new VM migration and never before used for the VM migration method. In the energy consumption scenario by VM migration, a FFO-based linear model is formulated that executes an FFO algorithm that is able to solve the power consumption problem with the firefly attraction feature. In other words, this paper proposes a virtual energy virtualization migration technique that emits live VMs from an active node to another active node. The proposed technique uses the biography-inspired worn-out optimization technique to find the best node for over-migrating VMs to achieve energy efficiency in cloud data centers. This optimizes energy efficiency through the optimal migration of VMs, thereby improving the level of resource utilization.
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