VMR: Virtual Machine Replacement Algorithm for QoS and Energy-Awareness in Cloud Data Centers

2017 ◽  
Author(s):  
Riaz Ali ◽  
Yao Shen ◽  
Xiangwei Huang ◽  
Jingyu Zhang ◽  
Ahmad Ali
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Energy Awareness ◽  
Cloud Data ◽  
Replacement Algorithm ◽  
Cloud Data Centers ◽  
Machine Replacement

scholarly journals Virtual Machine Placement Algorithm for Both Energy-Awareness and SLA Violation Reduction in Cloud Data Centers

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Zhou Zhou ◽  
Zhigang Hu ◽  
Keqin Li
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
High Energy ◽  
Energy Awareness ◽  
Cloud Data ◽  
Light Load ◽  
Sla Violation ◽  
Cloud Data Centers ◽  
Placement Algorithm

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.

scholarly journals Energy aware dynamic virtual machine consolidation in cloud data centers

2018 ◽  
Vol 7 (2.8) ◽  
pp. 550 ◽  
Author(s):  
G Anusha ◽  
P Supraja
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Energy Aware ◽  
Large Power ◽  
Complex Tasks ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Virtual Machine Consolidation ◽  
Cloud Data Centers

Cloud computing is a growing technology now-a-days, which provides various resources to perform complex tasks. These complex tasks can be performed with the help of datacenters. Data centers helps the incoming tasks by providing various resources like CPU, storage, network, bandwidth and memory, which has resulted in the increase of the total number of datacenters in the world. These data centers consume large volume of energy for performing the operations and which leads to high operation costs. Resources are the key cause for the power consumption in data centers along with the air and cooling systems. Energy consumption in data centers is comparative to the resource usage. Excessive amount of energy consumption by datacenters falls out in large power bills. There is a necessity to increase the energy efficiency of such data centers. We have proposed an Energy aware dynamic virtual machine consolidation (EADVMC) model which focuses on pm selection, vm selection, vm placement phases, which results in the reduced energy consumption and the Quality of service (QoS) to a considerable level.

Type-aware virtual machine management for energy efficient cloud data centers

2018 ◽  
Vol 19 ◽  
pp. 185-203 ◽  
Author(s):  
Auday Al-Dulaimy ◽  
Wassim Itani ◽  
Rached Zantout ◽  
Ahmed Zekri
Keyword(s):  
Virtual Machine ◽  
Energy Efficient ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers
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