  Cloud Computing paradigm has revolutionized IT industry and be able to offer
computing as the fifth utility. With the pay-as-you-go model, cloud computing
enables to offer the resources dynamically for customers anytime. Drawing the
attention from both academia and industry, cloud computing is viewed as one of
the backbones of the modern economy. However, the high energy consumption of
cloud data centers contributes to high operational costs and carbon emission to
the environment. Therefore, Green cloud computing is required to ensure energy
efficiency and sustainability, which can be achieved via energy efficient
techniques. One of the dominant approaches is to apply energy efficient
algorithms to optimize resource usage and energy consumption. Currently,
various virtual machine consolidation-based energy efficient algorithms have
been proposed to reduce the energy of cloud computing environment. However,
most of them are not compared comprehensively under the same scenario, and
their performance is not evaluated with the same experimental settings. This
makes users hard to select the appropriate algorithm for their objectives. To
provide insights for existing energy efficient algorithms and help researchers
to choose the most suitable algorithm, in this paper, we compare several
state-of-the-art energy efficient algorithms in depth from multiple
perspectives, including architecture, modelling and metrics. In addition, we
also implement and evaluate these algorithms with the same experimental
settings in CloudSim toolkit. The experimental results show the performance
comparison of these algorithms with comprehensive results. Finally, detailed
discussions of these algorithms are provided.
