An empirical evaluation of energy-aware load balancing technique for cloud data center

@article{Kansal2017AnEE,
  title={An empirical evaluation of energy-aware load balancing technique for cloud data center},
  author={Nidhi Jain Kansal and Inderveer Chana},
  journal={Cluster Computing},
  year={2017},
  pages={1-19}
}
Load balancing is one of the main challenges in cloud computing, to dynamically distribute the workload across multiple nodes to ensure that no node is either overloaded or underloaded. This paper presents a novel energy-aware load balancing technique that uses an amalgamation of the Artificial Bee Colony and the Firefly algorithms. This technique aspires to balance the load of the cloud infrastructure while trying to maximize the energy efficiency through the efficient usage of the cloud… CONTINUE READING

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Key Quantitative Results

  • It saved 40.47% of the average energy consumption, which is accomplished by improving CPU utilization level by 49.68%, memory utilization level by 24.41%, reducing VM migrations by 63.10% and saving 53.21% of nodes.
  • This saving is accomplished by improving CPU utilization level by 49.68%, memory utilization level by 24.41%, reducing VM migrations by 63.10% and saving 53.21% of nodes.

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