Minimizing Virtual Machine Migration Probability for Cloud Environments
Xiaojiao Chen, Shiping Chen, Fan‐Hsun Tseng, Li‐Der Chou, Han‐Chieh Chao · 2013
In recent years, many researchers focus on how to balance the number of average virtual machines (VMs) in cloud data center. However, the VMs suddenly jumped to more than the utilization threshold of the physical machine (PM) and exhausted the computing resource, which will lead to VM migration and extra cost. In this paper, the VM migration problem in cloud computing is formulated based on mixed integer linear programming (MILP). Then we propose an Allocation algorithm to distribute the VMs among the PMs, which is based on the Support Vector Machine (SVM). The ultimate goal that not only minimizes the VM migration probability but also maximizes the PM resources as much as possible. According to the training data in a period time, the classified VMs can be allocated into the PMs with the lowest VM migration probability by the proposed Allocation algorithm. The simulation results show that the proposed algorithm can achieve lower VM migration probability and reduce the cost for the cloud environments.