An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

2016 ◽  
Vol 29 (14) ◽  
pp. e3909 ◽  
Author(s):  
Wanchun Dou ◽  
Xiaolong Xu ◽  
Shunmei Meng ◽  
Xuyun Zhang ◽  
Chunhua Hu ◽  
...  
2017 ◽  
Vol 13 (2) ◽  
pp. 155014771769489 ◽  
Author(s):  
Guowen Xing ◽  
Xiaolong Xu ◽  
Haolong Xiang ◽  
Shengjun Xue ◽  
Sai Ji ◽  
...  

With the rapid resource requirements of Internet of Things applications, cloud computing technology is regarded as a promising paradigm for resource provision. To improve the efficiency and effectiveness of cloud services, it is essential to improve the resource fairness and achieve energy savings. However, it is still a challenge to schedule the virtual machines in an energy-efficient manner while taking into consideration the resource fairness. In view of this challenge, a fair energy-efficient virtual machine scheduling method for Internet of Things applications is designed in this article. Specifically, energy and fairness are analyzed in a formal way. Then, a virtual machine scheduling method is proposed to achieve the energy efficiency and further improve the resource fairness during the executions of Internet of Things applications. Finally, experimental evaluation demonstrates the validity of our proposed method.


2019 ◽  
Vol 23 (2) ◽  
pp. 1275-1297 ◽  
Author(s):  
Lianyong Qi ◽  
Yi Chen ◽  
Yuan Yuan ◽  
Shucun Fu ◽  
Xuyun Zhang ◽  
...  

2020 ◽  
Vol 105 ◽  
pp. 789-799 ◽  
Author(s):  
Xiaolong Xu ◽  
Xuyun Zhang ◽  
Maqbool Khan ◽  
Wanchun Dou ◽  
Shengjun Xue ◽  
...  

2014 ◽  
Vol 1046 ◽  
pp. 508-511
Author(s):  
Jian Rong Zhu ◽  
Yi Zhuang ◽  
Jing Li ◽  
Wei Zhu

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.


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