Background: Cloud computing is one of the prominent technology revolutions around
us. It is changing the ways the consumer expends services, changing the ways the organization develop
and run applications and is completely reshaping the old business models in multiple industries.
Cloud service providers need large-scale data centers for offering cloud resources to users, the
electric power consumed by these data centers has become a concrete and prudential concern. Most
of the energy is dissipated in these data centers due to under-utilized hosts, which also subsidies to
global warming. The broadly adept technology is virtual machine migration in cloud computing,
therefore, the main focus is to save energy.
Objective: Virtual Machine (VM) migration can reap various objectives like load balancing, ubiquitous
computing, power management, fault tolerance, server maintenance, etc. This paper presents an
energy-oriented mechanism for VM migration based on firefly optimization that reduces energy
consumption and the number of VM migrations to a great extent.
Methods: A Firefly Optimization (FFO) oriented VM migration mechanism has been proposed,
which allocates tasks to the physical machines in cloud data centers. It strives to migrates high loaded
VMs from one physical node to another, which induces minimum energy consumption after VM
Results: The empirical result shows that the FFO based mechanism, implemented in the CloudSim
simulator, performs better in terms of the number of hosts saved up to 13.91% in contrast to the
First Fit Decreasing (FFD) algorithm and 8.21% as compared to Ant Colony Optimization (ACO). It
reduced energy consumption up to 12.76% as compared to FFD and 7.78% as compared to ACO
and, ultimately lesser number of migrations up to 52.49% when compared to FFD and 44.51% as
compared to ACO.
Conclusion: The proposed scheme performs better in terms of saving hosts, reducing energy consumption,
and decreasing the number of migrations in contrast to FFD and ACO techniques. The
research paper also presents challenges and issues in cloud computing, VM migration process, VM
migration techniques, their comparative review as well.