DYNAMIC VIRTUAL MACHINE CONSOLIDATION FOR IMPROVING ENERGY EFFICIENCY IN CLOUD DATA CENTERS

With the advancement of cloud computing, the high energy consumption of cloud computing data centers has become a prominent problem. In this paper, we propose a new virtual machine consolidation framework for achieving better energy efficiency. The proposed framework has two main contributions: (1) In the underloaded host decision step, this paper proposes a new method based on the overload threshold of hosts and the average utilization of all active hosts, which is named Improved Underload Decision (IUD) algorithm; (2) And in the migration target host selection step, this paper puts forward a new strategy based on the average utilization of the data center, which is named Minimum Average Utilization Difference (MAUD) policy. The experimental result shows that the proposed algorithm can reduce the energy consumption and SLA violation of data centers compared with existing algorithms, thus improving the energy efficiency of data centers.