Energy-Saving Virtual Machine Placement in Cloud Data Centers

In cloud data centers, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, therefore, how to place VMs on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. The existing VM placement schemes are to optimize physical server resources utilization or network resources utilization, but few of them focuses on optimizing multiple resources utilization simultaneously. To address the issue, this paper proposes a VM placement scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to improve resource utilization and reduce both the number of active physical servers and network elements so as to finally reduce energy consumption. Since VM placement problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem, we design a novel greedy algorithm by combining minimum cut with the best-fit, and the simulations show that our solution achieves better results.