Hybrid biogeography algorithm for reducing power consumption in cloud computing
2017 International Conference on Advances in Computing …, 2017•ieeexplore.ieee.org
This paper purposes a hybrid biogeography algorithm that is the combination of
biogeography and particle swarm optimization algorithm. A hybrid algorithm performs
mutation, based on velocity instead of random mutation. The simulation results show that our
proposed scheduling algorithm reduces the energy footprint of cloud data centers and
improves a convergence rate of biogeography algorithm.
biogeography and particle swarm optimization algorithm. A hybrid algorithm performs
mutation, based on velocity instead of random mutation. The simulation results show that our
proposed scheduling algorithm reduces the energy footprint of cloud data centers and
improves a convergence rate of biogeography algorithm.
This paper purposes a hybrid biogeography algorithm that is the combination of biogeography and particle swarm optimization algorithm. A hybrid algorithm performs mutation, based on velocity instead of random mutation. The simulation results show that our proposed scheduling algorithm reduces the energy footprint of cloud data centers and improves a convergence rate of biogeography algorithm.
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