GMDS Algorithm based on OpenStack
Haixing Li, Yonggang Li, Zhizhong Zhang · 2018
With the rapid development of cloud computing, more and more users choose to deploy applications on virtual machines on the cloud platform. And OpenStack is one of the mainstream cloud platforms in cloud computing. The load imbalance of cloud platform is a concern. Live migration of virtual machines is an effective way to achieve load balancing and optimize resource utilization. With the rapid expansion of the scheduling domain of the cloud platform, the traditional centralized migration strategy begins to lack reliability and scalability. In this paper, we propose a new VM dynamic scheduling algorithm based on the grey Markov prediction model. In our algorithm, the gray Markov prediction model is used to predict the state of the node load information, so as to cooperate with the VM dynamic scheduling mechanism to properly migrate the VM, and set the upper and lower thresholds of the node to achieve load balancing and reduce energy consumption. The experimental results show that the GMDS algorithm achieves the reduction of energy consumption by implementing load balancing of the entire system and reducing the number of running nodes, which are superior to the existing migration strategy.