Workload-Aware and CPU Frequency Scaling for Optimal Energy Consumption in VM Allocation

Abstract

In the problem of VMs consolidation for cloud energy saving, different workloads will ask for different resources. Thus, considering workload characteristic, the VM placement solution will be more reasonable. In the real world, different workload works in a varied CPU utilization during its work time according to its task characteristics. That means energy consumption related to both the CPU utilization and CPU frequency. Therefore, only using the model of CPU frequency to evaluate energy consumption is insufficient. This paper theoretically verified that there will be a CPU frequency best suit for a certain CPU utilization in order to obtain the minimum energy consumption. According to this deduction, we put forward a heuristic CPU frequency scaling algorithm VP-FS (virtual machine placement with frequency scaling). In order to carry the experiments, we realized three typical greedy algorithms for VMs placement and simulate three groups of VM tasks. 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 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.