ELVMC: A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing

Da ming Zhao; Jian tao Zhou; Shucheng Yu

doi:10.1007/978-3-030-60239-0_5

ELVMC: A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing

Da ming Zhao
, Jian tao Zhou
, Shucheng Yu

Department of Electrical and Computer Engineering

Inner Mongolia University

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

2 Scopus citations

Abstract

Virtual machine consolidation (VMC) is a technology that aggregates virtual machines distributed on multiple physical machines into a small number of physical machines to improve resource utilization and energy efficiency of data center. However, excessive virtual machine aggregation and migration can also have a significant negative impact on performance. In this paper, an algorithm named ELVMC with multiple resource prediction is proposed for optimal virtual machine consolidation. It applies a modified Best-Fit Decreasing (BFD) algorithm for resource optimization at both overloaded hosts and underloaded hosts with consideration of load balancing. Different from current research, ELVMC aims to obtain an optimal virtual machine (VM) placement during each consolidation process by simultaneously optimizing multiple system performance metrics in terms of energy consumption, VM migrations and QoS guarantees while keeping the load balanced. Simulation results show that ELVMC is superior to the state of the arts, including the traditional BFD and SABFD-HS algorithms as well as recent research VMCUP-M and MUC-MBFD.

Original language	English
Title of host publication	Algorithms and Architectures for Parallel Processing - 20th International Conference, ICA3PP 2020, Proceedings
Editors	Meikang Qiu
Pages	62-81
Number of pages	20
DOIs	https://doi.org/10.1007/978-3-030-60239-0_5
State	Published - 2020
Event	20th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2020 - New York, United States Duration: 2 Oct 2020 → 4 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12453 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2020
Country/Territory	United States
City	New York
Period	2/10/20 → 4/10/20

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Energy consumption
Load balance
Resource prediction
Virtual machine consolidation
Virtual machine migration

Access to Document

10.1007/978-3-030-60239-0_5

Cite this

Zhao, D. M., Zhou, J. T., & Yu, S. (2020). ELVMC: A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing. In M. Qiu (Ed.), Algorithms and Architectures for Parallel Processing - 20th International Conference, ICA3PP 2020, Proceedings (pp. 62-81). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12453 LNCS). https://doi.org/10.1007/978-3-030-60239-0_5

Zhao, Da ming ; Zhou, Jian tao ; Yu, Shucheng. / ELVMC : A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing. Algorithms and Architectures for Parallel Processing - 20th International Conference, ICA3PP 2020, Proceedings. editor / Meikang Qiu. 2020. pp. 62-81 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{29246f8aaa6f4a3c83ff80236354eac9,

title = "ELVMC: A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing",

abstract = "Virtual machine consolidation (VMC) is a technology that aggregates virtual machines distributed on multiple physical machines into a small number of physical machines to improve resource utilization and energy efficiency of data center. However, excessive virtual machine aggregation and migration can also have a significant negative impact on performance. In this paper, an algorithm named ELVMC with multiple resource prediction is proposed for optimal virtual machine consolidation. It applies a modified Best-Fit Decreasing (BFD) algorithm for resource optimization at both overloaded hosts and underloaded hosts with consideration of load balancing. Different from current research, ELVMC aims to obtain an optimal virtual machine (VM) placement during each consolidation process by simultaneously optimizing multiple system performance metrics in terms of energy consumption, VM migrations and QoS guarantees while keeping the load balanced. Simulation results show that ELVMC is superior to the state of the arts, including the traditional BFD and SABFD-HS algorithms as well as recent research VMCUP-M and MUC-MBFD.",

keywords = "Energy consumption, Load balance, Resource prediction, Virtual machine consolidation, Virtual machine migration",

author = "Zhao, \{Da ming\} and Zhou, \{Jian tao\} and Shucheng Yu",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 20th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2020 ; Conference date: 02-10-2020 Through 04-10-2020",

year = "2020",

doi = "10.1007/978-3-030-60239-0\_5",

language = "English",

isbn = "9783030602383",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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}

Zhao, DM, Zhou, JT & Yu, S 2020, ELVMC: A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing. in M Qiu (ed.), Algorithms and Architectures for Parallel Processing - 20th International Conference, ICA3PP 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12453 LNCS, pp. 62-81, 20th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2020, New York, United States, 2/10/20. https://doi.org/10.1007/978-3-030-60239-0_5

ELVMC: A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing. / Zhao, Da ming; Zhou, Jian tao; Yu, Shucheng.
Algorithms and Architectures for Parallel Processing - 20th International Conference, ICA3PP 2020, Proceedings. ed. / Meikang Qiu. 2020. p. 62-81 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12453 LNCS).

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

TY - GEN

T1 - ELVMC

T2 - 20th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2020

AU - Zhao, Da ming

AU - Zhou, Jian tao

AU - Yu, Shucheng

PY - 2020

Y1 - 2020

N2 - Virtual machine consolidation (VMC) is a technology that aggregates virtual machines distributed on multiple physical machines into a small number of physical machines to improve resource utilization and energy efficiency of data center. However, excessive virtual machine aggregation and migration can also have a significant negative impact on performance. In this paper, an algorithm named ELVMC with multiple resource prediction is proposed for optimal virtual machine consolidation. It applies a modified Best-Fit Decreasing (BFD) algorithm for resource optimization at both overloaded hosts and underloaded hosts with consideration of load balancing. Different from current research, ELVMC aims to obtain an optimal virtual machine (VM) placement during each consolidation process by simultaneously optimizing multiple system performance metrics in terms of energy consumption, VM migrations and QoS guarantees while keeping the load balanced. Simulation results show that ELVMC is superior to the state of the arts, including the traditional BFD and SABFD-HS algorithms as well as recent research VMCUP-M and MUC-MBFD.

AB - Virtual machine consolidation (VMC) is a technology that aggregates virtual machines distributed on multiple physical machines into a small number of physical machines to improve resource utilization and energy efficiency of data center. However, excessive virtual machine aggregation and migration can also have a significant negative impact on performance. In this paper, an algorithm named ELVMC with multiple resource prediction is proposed for optimal virtual machine consolidation. It applies a modified Best-Fit Decreasing (BFD) algorithm for resource optimization at both overloaded hosts and underloaded hosts with consideration of load balancing. Different from current research, ELVMC aims to obtain an optimal virtual machine (VM) placement during each consolidation process by simultaneously optimizing multiple system performance metrics in terms of energy consumption, VM migrations and QoS guarantees while keeping the load balanced. Simulation results show that ELVMC is superior to the state of the arts, including the traditional BFD and SABFD-HS algorithms as well as recent research VMCUP-M and MUC-MBFD.

KW - Energy consumption

KW - Load balance

KW - Resource prediction

KW - Virtual machine consolidation

KW - Virtual machine migration

UR - https://www.scopus.com/pages/publications/85092696324

UR - https://www.scopus.com/pages/publications/85092696324#tab=citedBy

U2 - 10.1007/978-3-030-60239-0_5

DO - 10.1007/978-3-030-60239-0_5

M3 - Conference contribution

AN - SCOPUS:85092696324

SN - 9783030602383

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

SP - 62

EP - 81

BT - Algorithms and Architectures for Parallel Processing - 20th International Conference, ICA3PP 2020, Proceedings

A2 - Qiu, Meikang

Y2 - 2 October 2020 through 4 October 2020

ER -

Zhao DM, Zhou JT, Yu S. ELVMC: A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing. In Qiu M, editor, Algorithms and Architectures for Parallel Processing - 20th International Conference, ICA3PP 2020, Proceedings. 2020. p. 62-81. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-60239-0_5

ELVMC: A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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