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  • Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy - Accepted

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Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy

Research output: Contribution to journalJournal article

Published

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Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy. / Li, Xiang; Garraghan, Peter; Jiang, Xiaohong; Wu, Zhaohui; Xu, Jie.

In: IEEE Transactions on Parallel and Distributed Systems, Vol. 29, No. 6, 01.06.2018, p. 1317-1331.

Research output: Contribution to journalJournal article

Harvard

Li, X, Garraghan, P, Jiang, X, Wu, Z & Xu, J 2018, 'Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy', IEEE Transactions on Parallel and Distributed Systems, vol. 29, no. 6, pp. 1317-1331. https://doi.org/10.1109/TPDS.2017.2688445

APA

Li, X., Garraghan, P., Jiang, X., Wu, Z., & Xu, J. (2018). Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy. IEEE Transactions on Parallel and Distributed Systems, 29(6), 1317-1331. https://doi.org/10.1109/TPDS.2017.2688445

Vancouver

Li X, Garraghan P, Jiang X, Wu Z, Xu J. Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy. IEEE Transactions on Parallel and Distributed Systems. 2018 Jun 1;29(6):1317-1331. https://doi.org/10.1109/TPDS.2017.2688445

Author

Li, Xiang ; Garraghan, Peter ; Jiang, Xiaohong ; Wu, Zhaohui ; Xu, Jie. / Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy. In: IEEE Transactions on Parallel and Distributed Systems. 2018 ; Vol. 29, No. 6. pp. 1317-1331.

Bibtex

@article{5b8b132c7a7747f3b2cb8ac63b9ca8ff,
title = "Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy",
abstract = "Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% - 43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty.",
keywords = "Cloud computing, energy efficiency, datacenter modeling, workload scheduling, virtual machine",
author = "Xiang Li and Peter Garraghan and Xiaohong Jiang and Zhaohui Wu and Jie Xu",
note = "{\textcopyright}2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2018",
month = jun,
day = "1",
doi = "10.1109/TPDS.2017.2688445",
language = "English",
volume = "29",
pages = "1317--1331",
journal = "IEEE Transactions on Parallel and Distributed Systems",
issn = "1045-9219",
publisher = "IEEE Computer Society",
number = "6",

}

RIS

TY - JOUR

T1 - Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy

AU - Li, Xiang

AU - Garraghan, Peter

AU - Jiang, Xiaohong

AU - Wu, Zhaohui

AU - Xu, Jie

N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2018/6/1

Y1 - 2018/6/1

N2 - Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% - 43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty.

AB - Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% - 43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty.

KW - Cloud computing

KW - energy efficiency

KW - datacenter modeling

KW - workload scheduling

KW - virtual machine

U2 - 10.1109/TPDS.2017.2688445

DO - 10.1109/TPDS.2017.2688445

M3 - Journal article

VL - 29

SP - 1317

EP - 1331

JO - IEEE Transactions on Parallel and Distributed Systems

JF - IEEE Transactions on Parallel and Distributed Systems

SN - 1045-9219

IS - 6

ER -