An instances placement algorithm based on disk I/O load for big data in private cloud
Jian Chun Guo, Zhaomeng Zhu, Xiumin Zhou, Gongxuan Zhang · 2012
In generally, large companies or organizations have the demand of big data processing and they do not want to entrust their business processes and data to third parties (Amazon, Google, etc.). The private cloud could meet their needs. In private cloud, tasks run at multiple instances (also known as virtual machines), which could be paced in different physical nodes. Obviously, the instances which be used to process big data need higher CPU and disk performance than other kinds of instances. If the instances of disk resource consuming are placed in the same physical node, clearly, the disk I/O bandwidth would be used up quickly that would affect the performance of the entire node seriously. This paper proposes an instances placement algorithm FFDL that based on disk I/O for private cloud environment to deal with big data that would adopt the disk I/O load balancing strategy and reduce competition for the disk I/O load between instances. We have validated our approach by conducting a performance evaluation study on the open source private cloud platform—Openstack. The results demonstrate that our algorithm has immense potential as it offers significant computation time savings than the Greedy algorithm and demonstrates high potential for the improvement of disk I/O load balancing in the entire private cloud system for the big data.