Abstract
The problem of high energy consumption is becoming more and more serious due to the construction of large-scale cloud data centers. In order to reduce the energy consumption and SLA violation, a new virtual machine (VM) placement algorithm named ATEA (adaptive three-threshold energy-aware algorithm), which takes good use of the historical data from resource usage by VMs, is presented. In ATEA, according to the load handled, data center hosts are divided into four classes: hosts with little load, hosts with light load, hosts with moderate load, and hosts with heavy load. ATEA migrates VMs on heavily loaded or little-loaded hosts to lightly loaded hosts, while the VMs on lightly loaded and moderately loaded hosts remain unchanged. Then, on the basis of ATEA, two kinds of adaptive three-threshold algorithm and three kinds of VMs selection policies are proposed. Finally, we verify the effectiveness of the proposed algorithms by CloudSim toolkit utilizing real-world workload. The experimental results show that the proposed algorithms efficiently reduce energy consumption and SLA violation.
Highlights
Cloud computing [1, 2] is derived from grid computing
The construction of a large-scale virtualized data centers meets the demand of computational power; on the other hand, such data centers consume a great many of electrical energy resources, leading to high energy consumption and carbon dioxide emissions
The high energy consumption problem of virtualized data centers causes a series of problems, including energy wastes, low Return on the Investment (ROI), system instability, and more carbon dioxide emissions [4]
Summary
Cloud computing [1, 2] is derived from grid computing. At present, cloud computing is receiving more and more attention, through which people can access resources in a simple way. The high energy consumption problem of virtualized data centers causes a series of problems, including energy wastes, low Return on the Investment (ROI), system instability, and more carbon dioxide emissions [4]. It is extremely necessary to reduce the energy consumption of data centers while keeping low SLA (Service Level Agreement) violation [7]. We put forward a new VM deployment algorithm (ATEA), two kinds of adaptive three-threshold algorithm (KAM and KAI), and three kinds of VM selection policies to reduce energy consumption and SLA violation.
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