Minimizing the Energy Consumption of Cloud Computing Data Centers Using Queueing Theory

被引:2
作者
Kumar, Ranjan [1 ]
Sahoo, G. [2 ]
Yadav, Vikram [3 ]
Malik, Pooja [3 ]
机构
[1] Cambridge Inst Technol, Dept Comp Sci & Engn, Ranchi, Bihar, India
[2] Birla Inst Technol, Ranchi, Bihar, India
[3] IBM Corp, Delhi, India
来源
ADVANCES IN COMPUTATIONAL INTELLIGENCE | 2017年 / 509卷
关键词
Energy optimization; Cloud computing; Ant colony optimization; Bee colony optimization; Queueing theory; PowerNap; Cloud clusters; Algorithms;
D O I
10.1007/978-981-10-2525-9_20
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The servers utilize a major proportion of the energy utilized in the cloud-based companies. Through this paper, we are proposing a group of methodologies and approaches via which it would be possible to attain an energy conservation of more than 80 % of the total energy consumed by the cluster. We are using an integrated mechanism in between the servers and the jobs, consisting of combination of optimization approaches: beginning from ant colony optimization in the cloud cluster to bee colony optimization in load balancing to queueing theory in solving different job arrival patterns and some intermediate optimization algorithms like PowerNap and more. We have designed this approach without degrading the quality of service of the datacenters/clusters.
引用
收藏
页码:201 / 210
页数:10
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