PageRankVM: A PageRank Based Algorithm with Anti-Collocation Constraints for Virtual Machine Placement in Cloud Datacenters

There is a dramatic increase in the variety of virtual machines (VMs) and complexity of VM placement problems in clouds. Previous VM placement approaches attempt to accommodate more VMs efficiently on fewer PMs by balancing the resource usages across multiple dimensions. However, these approaches are not sufficiently accurate in measuring the quality of the PMs in terms of fully utilizing PM resource and having the potential to accommodate more VMs. Therefore, it is critical to design a new method that can more accurately measure the probability of a PM of fully utilizing its resources after accommodating a given VM with the consideration of different types of VMs. In addition, anti-collocation constraints must be handled efficiently. We propose a PageRank based VM placement algorithm with anti-collocation constraints (PageRankVM). PageRankVM defines the best PM resource usage profile, which means that the PM has full resource utilization for every resource dimension, and then ranks PM profiles according to their convergence of transferring (by accommodating VMs) to the best profile. PageRankVM then places a given VM to the PM based on the ranks of the resulted PM profiles after accommodating the VM with the consideration of anti-collocation constraints. Compared to previous approaches, PageRankVM effectively measures the ability of different PM profiles to reach the best profiles by accommodating a given VM, and hence differentiates the effectiveness of different VM placement decisions. We conducted extensive trace-driven simulation and GENI testbed experiments and demonstrated that PageRankVM has superior performance compared with other methods in terms of reducing the number of PMs, the energy consumption, the number of VM migrations, and the service level objective (SLO) violations.