Towards optimal CPU frequency and different workload for multi-objective VM allocation

In the problem of VMs consolidation for cloud energy saving, workload characteristics should be considered to make a more reasonable solution for VM placement. Different workload works in a varied CPU utilization during its work time according to its task characteristics. However, there are many works that evaluate energy consumption have the basic assumption that the CPU works on 100% full load. In this paper, we have theoretically verified that there will be a CPU frequency best suited for a certain CPU utilization that can make the minimum energy consumption. According to this deduction, We put forward a CPU frequency scaling algorithm VP-FS(Virtual machine Placement with Frequency Scaling). We simulate three groups of VM tasks. We also design and implement three typical greedy algorithms for VMs placement. We then carry the experiments using these four algorithms to allocate the three groups of VMs respectively. Our efforts show that different workloads will affect VMs allocation results. Each group of workload has its most suitable algorithm when considering the minimum used number of physical machines. And because of the CPU frequency scaling, VP-FS has the best results on the total energy consumption compared with the other three algorithms under any of the three groups of workloads.