Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers

Zhe Ding; Yu Chu Tian; Maolin Tang; You Gan Wang; Zu Guo Yu; Jiong Jin; Weizhe Zhang

doi:10.1007/978-981-99-8082-6_2

Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers

Zhe Ding
, Yu Chu Tian^*
, Maolin Tang
, You Gan Wang
, Zu Guo Yu
, Jiong Jin
, Weizhe Zhang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Energy efficiency is crucial for the operation and management of cloud data centers, which are the foundation of cloud computing. Virtual machine (VM) placement plays a vital role in improving energy efficiency in data centers. The genetic algorithm (GA) has been extensively studied for solving the VM placement problem due to its ability to provide high-quality solutions. However, GA’s high computational demands limit further improvement in energy efficiency, where a fast and lightweight solution is required. This paper presents an adaptive population control scheme that enhances gene diversity through population control, adaptive mutation rate, and accelerated termination. Experimental results show that our scheme achieves a 17% faster acceleration and 49% fewer generations compared to the standard GA for energy-efficient VM placement in large-scale data centers.

Original language	English
Title of host publication	Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings
Editors	Biao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	14-26
Number of pages	13
ISBN (Print)	9789819980819
DOIs	https://doi.org/10.1007/978-981-99-8082-6_2
State	Published - 2024
Externally published	Yes
Event	30th International Conference on Neural Information Processing, ICONIP 2023 - Changsha, China Duration: 20 Nov 2023 → 23 Nov 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14448 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	30th International Conference on Neural Information Processing, ICONIP 2023
Country/Territory	China
City	Changsha
Period	20/11/23 → 23/11/23

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Data center
energy efficiency
genetic algorithm
population
virtual machine

Access to Document

10.1007/978-981-99-8082-6_2

Cite this

Ding, Z., Tian, Y. C., Tang, M., Wang, Y. G., Yu, Z. G., Jin, J., & Zhang, W. (2024). Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers. In B. Luo, L. Cheng, Z.-G. Wu, H. Li, & C. Li (Eds.), Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings (pp. 14-26). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14448 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8082-6_2

Ding, Zhe ; Tian, Yu Chu ; Tang, Maolin et al. / Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers. Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. editor / Biao Luo ; Long Cheng ; Zheng-Guang Wu ; Hongyi Li ; Chaojie Li. Springer Science and Business Media Deutschland GmbH, 2024. pp. 14-26 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{8020aee2c0304842a5e90adcc51704f1,

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abstract = "Energy efficiency is crucial for the operation and management of cloud data centers, which are the foundation of cloud computing. Virtual machine (VM) placement plays a vital role in improving energy efficiency in data centers. The genetic algorithm (GA) has been extensively studied for solving the VM placement problem due to its ability to provide high-quality solutions. However, GA{\textquoteright}s high computational demands limit further improvement in energy efficiency, where a fast and lightweight solution is required. This paper presents an adaptive population control scheme that enhances gene diversity through population control, adaptive mutation rate, and accelerated termination. Experimental results show that our scheme achieves a 17\% faster acceleration and 49\% fewer generations compared to the standard GA for energy-efficient VM placement in large-scale data centers.",

keywords = "Data center, energy efficiency, genetic algorithm, population, virtual machine",

author = "Zhe Ding and Tian, \{Yu Chu\} and Maolin Tang and Wang, \{You Gan\} and Yu, \{Zu Guo\} and Jiong Jin and Weizhe Zhang",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 30th International Conference on Neural Information Processing, ICONIP 2023 ; Conference date: 20-11-2023 Through 23-11-2023",

year = "2024",

doi = "10.1007/978-981-99-8082-6\_2",

language = "英语",

isbn = "9789819980819",

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Ding, Z, Tian, YC, Tang, M, Wang, YG, Yu, ZG, Jin, J & Zhang, W 2024, Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers. in B Luo, L Cheng, Z-G Wu, H Li & C Li (eds), Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14448 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 14-26, 30th International Conference on Neural Information Processing, ICONIP 2023, Changsha, China, 20/11/23. https://doi.org/10.1007/978-981-99-8082-6_2

Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers. / Ding, Zhe; Tian, Yu Chu; Tang, Maolin et al.
Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. ed. / Biao Luo; Long Cheng; Zheng-Guang Wu; Hongyi Li; Chaojie Li. Springer Science and Business Media Deutschland GmbH, 2024. p. 14-26 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14448 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers

AU - Ding, Zhe

AU - Tian, Yu Chu

AU - Tang, Maolin

AU - Wang, You Gan

AU - Yu, Zu Guo

AU - Jin, Jiong

AU - Zhang, Weizhe

PY - 2024

Y1 - 2024

N2 - Energy efficiency is crucial for the operation and management of cloud data centers, which are the foundation of cloud computing. Virtual machine (VM) placement plays a vital role in improving energy efficiency in data centers. The genetic algorithm (GA) has been extensively studied for solving the VM placement problem due to its ability to provide high-quality solutions. However, GA’s high computational demands limit further improvement in energy efficiency, where a fast and lightweight solution is required. This paper presents an adaptive population control scheme that enhances gene diversity through population control, adaptive mutation rate, and accelerated termination. Experimental results show that our scheme achieves a 17% faster acceleration and 49% fewer generations compared to the standard GA for energy-efficient VM placement in large-scale data centers.

AB - Energy efficiency is crucial for the operation and management of cloud data centers, which are the foundation of cloud computing. Virtual machine (VM) placement plays a vital role in improving energy efficiency in data centers. The genetic algorithm (GA) has been extensively studied for solving the VM placement problem due to its ability to provide high-quality solutions. However, GA’s high computational demands limit further improvement in energy efficiency, where a fast and lightweight solution is required. This paper presents an adaptive population control scheme that enhances gene diversity through population control, adaptive mutation rate, and accelerated termination. Experimental results show that our scheme achieves a 17% faster acceleration and 49% fewer generations compared to the standard GA for energy-efficient VM placement in large-scale data centers.

KW - Data center

KW - energy efficiency

KW - genetic algorithm

KW - population

KW - virtual machine

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SN - 9789819980819

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings

A2 - Luo, Biao

A2 - Cheng, Long

A2 - Wu, Zheng-Guang

A2 - Li, Hongyi

A2 - Li, Chaojie

PB - Springer Science and Business Media Deutschland GmbH

T2 - 30th International Conference on Neural Information Processing, ICONIP 2023

Y2 - 20 November 2023 through 23 November 2023

ER -

Ding Z, Tian YC, Tang M, Wang YG, Yu ZG, Jin J et al. Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers. In Luo B, Cheng L, Wu ZG, Li H, Li C, editors, Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 14-26. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-8082-6_2