Paper Info
Title
Adaptive VM Management with Two Phase Power Consumption Cost Models in Cloud Datacenter.
Abstract
As cloud computing models have evolved from clusters to large-scale data centers, reducing the energy consumption, which is a large part of the overall operating expense of data centers, has received much attention lately. From a cluster-level viewpoint, the most popular method for an energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers that do not have any virtual resources running. To maximize the energy efficiency with DRS, one of the primary adaptive resource management strategies is a Virtual Machine (VM) migration which consolidates VM instances into as few servers as possible. In this paper, we propose a Two Phase based Adaptive Resource Management (TP-ARM) scheme that migrates VM instances from under-utilized servers that are supposed to be turned off to sustainable ones based on their monitored resource utilizations in real time. In addition, we designed a Self-Adjusting Workload Prediction (SAWP) method to improve the forecasting accuracy of resource utilization even under irregular demand patterns. From the experimental results using real cloud servers, we show that our proposed schemes provide the superior performance of energy consumption, resource utilization and job completion time over existing resource allocation schemes.
Year
DOI
Venue
2016
10.1007/s11036-016-0690-z
MONET
Keywords
Field
DocType
Cloud computing,Virtual machine migration,Dynamic right sizing,Energy saving
Resource management,Demand patterns,Virtual machine,Efficient energy use,Computer science,Server,Computer network,Resource allocation,Energy consumption,Distributed computing,Cloud computing
Journal
Volume
Issue
ISSN
21
5
1383-469X
Citations 
PageRank 
References 
2
0.49
11
Authors
6
Name
Order
Citations
PageRank
Dong-Ki Kang1397.69
Fawaz Al Hazemi2236.02
Seong Hwan Kim333431.76
Min Chen42369142.44
Li-Mei Peng511723.37
Chan-hyun Youn623842.68