Dynamic virtual machine consolidation for energy efficient cloud data centers

Dong Ki Kang; Fawaz Alhazemi; Seong Hwan Kim; Chan Hyun Youn

doi:10.1007/978-3-319-38904-2_8

Dynamic virtual machine consolidation for energy efficient cloud data centers

Dong Ki Kang
, Fawaz Alhazemi
, Seong Hwan Kim
, Chan Hyun Youn^*

^*Corresponding author for this work

Department of Computer Science & Artificial Intelligence

Korea Advanced Institute of Science and Technology

Research output: Contribution to conference › Conference paper › peer-review

7 Scopus citations

Abstract

As a cloud computing model have led clusters to the large-scale data centers, reducing of the energy consumption which imposes a crucial part of the whole operating expense for data centers has received a lot of attention of a wide public. At cluster-level viewpoint, the most popular method for energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers those do not have any of running virtual resources. To maximize the energy efficiency through DRS, one of primary adaptive resource management strategies is a Virtual Machine (VM) consolidation which integrates VM instances into as few servers as possible. In this paper, we propose Virtual machine Consolidation based Size Decision (VC-SD) approach migrates VM instances from under-utilized servers which are supposed to be turned off to sustaining ones according to their monitored resource utilizations in real time. In addition, we design a Self Adjusting Workload Prediction (SAWP) method to improve a forecasting accuracy of resource utilization even under irregular demand patterns. Through experimental results based on real cloud servers, we show various metrics such as resource utilization, energy consumption and switching overhead caused by application processing, VM migration and DRS execution to verify a necessity of our proposed methodologies.

Original language	English
Title of host publication	Cloud Computing - 6th International Conference, CloudComp 2015
Editors	Yin Zhang, Chan-Hyun Youn, Limei Peng
Publisher	Springer Verlag
Pages	70-80
Number of pages	11
ISBN (Print)	9783319389035
DOIs	https://doi.org/10.1007/978-3-319-38904-2_8
State	Published - 2016
Event	6th International Conference on Cloud Computing, CloudComp 2015 - Daejeon, Korea, Republic of Duration: 2015.10.28 → 2015.10.29

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	167
ISSN (Print)	1867-8211

Conference

Conference	6th International Conference on Cloud Computing, CloudComp 2015
Country/Territory	Korea, Republic of
City	Daejeon
Period	15.10.28 → 15.10.29

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Cloud computing
Dynamic right sizing
Virtual machine migration
Workload prediction

Access to Document

10.1007/978-3-319-38904-2_8

Cite this

Kang, D. K., Alhazemi, F., Kim, S. H., & Youn, C. H. (2016). Dynamic virtual machine consolidation for energy efficient cloud data centers. In Y. Zhang, C.-H. Youn, & L. Peng (Eds.), Cloud Computing - 6th International Conference, CloudComp 2015 (pp. 70-80). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 167). Springer Verlag. https://doi.org/10.1007/978-3-319-38904-2_8

Kang, Dong Ki ; Alhazemi, Fawaz ; Kim, Seong Hwan et al. / Dynamic virtual machine consolidation for energy efficient cloud data centers. Cloud Computing - 6th International Conference, CloudComp 2015. editor / Yin Zhang ; Chan-Hyun Youn ; Limei Peng. Springer Verlag, 2016. pp. 70-80 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{9cf4f0bfc0204beea9ab293462062d89,

title = "Dynamic virtual machine consolidation for energy efficient cloud data centers",

abstract = "As a cloud computing model have led clusters to the large-scale data centers, reducing of the energy consumption which imposes a crucial part of the whole operating expense for data centers has received a lot of attention of a wide public. At cluster-level viewpoint, the most popular method for energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers those do not have any of running virtual resources. To maximize the energy efficiency through DRS, one of primary adaptive resource management strategies is a Virtual Machine (VM) consolidation which integrates VM instances into as few servers as possible. In this paper, we propose Virtual machine Consolidation based Size Decision (VC-SD) approach migrates VM instances from under-utilized servers which are supposed to be turned off to sustaining ones according to their monitored resource utilizations in real time. In addition, we design a Self Adjusting Workload Prediction (SAWP) method to improve a forecasting accuracy of resource utilization even under irregular demand patterns. Through experimental results based on real cloud servers, we show various metrics such as resource utilization, energy consumption and switching overhead caused by application processing, VM migration and DRS execution to verify a necessity of our proposed methodologies.",

keywords = "Cloud computing, Dynamic right sizing, Virtual machine migration, Workload prediction",

author = "Kang, \{Dong Ki\} and Fawaz Alhazemi and Kim, \{Seong Hwan\} and Youn, \{Chan Hyun\}",

note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.; 6th International Conference on Cloud Computing, CloudComp 2015 ; Conference date: 28-10-2015 Through 29-10-2015",

year = "2016",

doi = "10.1007/978-3-319-38904-2\_8",

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series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

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Kang, DK, Alhazemi, F, Kim, SH & Youn, CH 2016, Dynamic virtual machine consolidation for energy efficient cloud data centers. in Y Zhang, C-H Youn & L Peng (eds), Cloud Computing - 6th International Conference, CloudComp 2015. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 167, Springer Verlag, pp. 70-80, 6th International Conference on Cloud Computing, CloudComp 2015, Daejeon, Korea, Republic of, 15.10.28. https://doi.org/10.1007/978-3-319-38904-2_8

Dynamic virtual machine consolidation for energy efficient cloud data centers. / Kang, Dong Ki; Alhazemi, Fawaz; Kim, Seong Hwan et al.
Cloud Computing - 6th International Conference, CloudComp 2015. ed. / Yin Zhang; Chan-Hyun Youn; Limei Peng. Springer Verlag, 2016. p. 70-80 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 167).

Research output: Contribution to conference › Conference paper › peer-review

TY - GEN

T1 - Dynamic virtual machine consolidation for energy efficient cloud data centers

AU - Kang, Dong Ki

AU - Alhazemi, Fawaz

AU - Kim, Seong Hwan

AU - Youn, Chan Hyun

N1 - Publisher Copyright: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.

PY - 2016

Y1 - 2016

N2 - As a cloud computing model have led clusters to the large-scale data centers, reducing of the energy consumption which imposes a crucial part of the whole operating expense for data centers has received a lot of attention of a wide public. At cluster-level viewpoint, the most popular method for energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers those do not have any of running virtual resources. To maximize the energy efficiency through DRS, one of primary adaptive resource management strategies is a Virtual Machine (VM) consolidation which integrates VM instances into as few servers as possible. In this paper, we propose Virtual machine Consolidation based Size Decision (VC-SD) approach migrates VM instances from under-utilized servers which are supposed to be turned off to sustaining ones according to their monitored resource utilizations in real time. In addition, we design a Self Adjusting Workload Prediction (SAWP) method to improve a forecasting accuracy of resource utilization even under irregular demand patterns. Through experimental results based on real cloud servers, we show various metrics such as resource utilization, energy consumption and switching overhead caused by application processing, VM migration and DRS execution to verify a necessity of our proposed methodologies.

AB - As a cloud computing model have led clusters to the large-scale data centers, reducing of the energy consumption which imposes a crucial part of the whole operating expense for data centers has received a lot of attention of a wide public. At cluster-level viewpoint, the most popular method for energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers those do not have any of running virtual resources. To maximize the energy efficiency through DRS, one of primary adaptive resource management strategies is a Virtual Machine (VM) consolidation which integrates VM instances into as few servers as possible. In this paper, we propose Virtual machine Consolidation based Size Decision (VC-SD) approach migrates VM instances from under-utilized servers which are supposed to be turned off to sustaining ones according to their monitored resource utilizations in real time. In addition, we design a Self Adjusting Workload Prediction (SAWP) method to improve a forecasting accuracy of resource utilization even under irregular demand patterns. Through experimental results based on real cloud servers, we show various metrics such as resource utilization, energy consumption and switching overhead caused by application processing, VM migration and DRS execution to verify a necessity of our proposed methodologies.

KW - Cloud computing

KW - Dynamic right sizing

KW - Virtual machine migration

KW - Workload prediction

UR - https://www.scopus.com/pages/publications/84969269175

U2 - 10.1007/978-3-319-38904-2_8

DO - 10.1007/978-3-319-38904-2_8

M3 - Conference paper

AN - SCOPUS:84969269175

SN - 9783319389035

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 70

EP - 80

BT - Cloud Computing - 6th International Conference, CloudComp 2015

A2 - Zhang, Yin

A2 - Youn, Chan-Hyun

A2 - Peng, Limei

PB - Springer Verlag

T2 - 6th International Conference on Cloud Computing, CloudComp 2015

Y2 - 28 October 2015 through 29 October 2015

ER -

Kang DK, Alhazemi F, Kim SH, Youn CH. Dynamic virtual machine consolidation for energy efficient cloud data centers. In Zhang Y, Youn CH, Peng L, editors, Cloud Computing - 6th International Conference, CloudComp 2015. Springer Verlag. 2016. p. 70-80. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-319-38904-2_8

Dynamic virtual machine consolidation for energy efficient cloud data centers

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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