Adaptive VM Management with Two Phase Power Consumption Cost Models in Cloud Datacenter

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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
Publisher
SPRINGER
Issue Date
2016-10
Language
English
Article Type
Article
Keywords

VIRTUAL MACHINES; MOBILE CLOUD; TECHNOLOGIES; 5G

Citation

MOBILE NETWORKS & APPLICATIONS, v.21, no.5, pp.793 - 805

ISSN
1383-469X
DOI
10.1007/s11036-016-0690-z
URI
http://hdl.handle.net/10203/214257
Appears in Collection
EE-Journal Papers(저널논문)
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