Performance and energy-based cost prediction of virtual machines live migration in clouds

Moahammad Aldosaary; Karim Djemame

doi:10.5220/0006682803840391

Performance and energy-based cost prediction of virtual machines live migration in clouds

Moahammad Aldosaary
, Karim Djemame

Computer Engineering

University of Leeds

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

11 Scopus citations

Abstract

Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the workload, power consumption and total cost for heterogeneous VMs before and after live migration, with the possibility of recovering the migration cost e.g. 28.48% for the predicted cost recovery of the VM.

Original language	English
Title of host publication	CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science
Editors	Victor Mendez Munoz, Donald Ferguson, Markus Helfert, Claus Pahl
Publisher	SciTePress
Pages	384-391
Number of pages	8
ISBN (Electronic)	9789897582950
DOIs	https://doi.org/10.5220/0006682803840391
State	Published - 2018
Event	8th International Conference on Cloud Computing and Services Science, CLOSER 2018 - Funchal, Madeira, Portugal Duration: 19 Mar 2018 → 21 Mar 2018

Publication series

Name	CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science
Volume	2018-January

Conference

Conference	8th International Conference on Cloud Computing and Services Science, CLOSER 2018
Country/Territory	Portugal
City	Funchal, Madeira
Period	19/03/18 → 21/03/18

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Cloud computing
Cost prediction
Live migration
Power consumption
Workload prediction

Access to Document

10.5220/0006682803840391

Cite this

Aldosaary, M., & Djemame, K. (2018). Performance and energy-based cost prediction of virtual machines live migration in clouds. In V. M. Munoz, D. Ferguson, M. Helfert, & C. Pahl (Eds.), CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science (pp. 384-391). (CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science; Vol. 2018-January). SciTePress. https://doi.org/10.5220/0006682803840391

Aldosaary, Moahammad ; Djemame, Karim. / Performance and energy-based cost prediction of virtual machines live migration in clouds. CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science. editor / Victor Mendez Munoz ; Donald Ferguson ; Markus Helfert ; Claus Pahl. SciTePress, 2018. pp. 384-391 (CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science).

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title = "Performance and energy-based cost prediction of virtual machines live migration in clouds",

abstract = "Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the workload, power consumption and total cost for heterogeneous VMs before and after live migration, with the possibility of recovering the migration cost e.g. 28.48\% for the predicted cost recovery of the VM.",

keywords = "Cloud computing, Cost prediction, Live migration, Power consumption, Workload prediction",

author = "Moahammad Aldosaary and Karim Djemame",

note = "Publisher Copyright: Copyright {\textcopyright} 2018 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 8th International Conference on Cloud Computing and Services Science, CLOSER 2018 ; Conference date: 19-03-2018 Through 21-03-2018",

year = "2018",

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language = "English",

series = "CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science",

publisher = "SciTePress",

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Aldosaary, M & Djemame, K 2018, Performance and energy-based cost prediction of virtual machines live migration in clouds. in VM Munoz, D Ferguson, M Helfert & C Pahl (eds), CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science. CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science, vol. 2018-January, SciTePress, pp. 384-391, 8th International Conference on Cloud Computing and Services Science, CLOSER 2018, Funchal, Madeira, Portugal, 19/03/18. https://doi.org/10.5220/0006682803840391

Performance and energy-based cost prediction of virtual machines live migration in clouds. / Aldosaary, Moahammad; Djemame, Karim.
CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science. ed. / Victor Mendez Munoz; Donald Ferguson; Markus Helfert; Claus Pahl. SciTePress, 2018. p. 384-391 (CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science; Vol. 2018-January).

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

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AU - Aldosaary, Moahammad

AU - Djemame, Karim

PY - 2018

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N2 - Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the workload, power consumption and total cost for heterogeneous VMs before and after live migration, with the possibility of recovering the migration cost e.g. 28.48% for the predicted cost recovery of the VM.

AB - Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the workload, power consumption and total cost for heterogeneous VMs before and after live migration, with the possibility of recovering the migration cost e.g. 28.48% for the predicted cost recovery of the VM.

KW - Cloud computing

KW - Cost prediction

KW - Live migration

KW - Power consumption

KW - Workload prediction

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T3 - CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science

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BT - CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science

A2 - Munoz, Victor Mendez

A2 - Ferguson, Donald

A2 - Helfert, Markus

A2 - Pahl, Claus

PB - SciTePress

T2 - 8th International Conference on Cloud Computing and Services Science, CLOSER 2018

Y2 - 19 March 2018 through 21 March 2018

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Aldosaary M, Djemame K. Performance and energy-based cost prediction of virtual machines live migration in clouds. In Munoz VM, Ferguson D, Helfert M, Pahl C, editors, CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science. SciTePress. 2018. p. 384-391. (CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science). doi: 10.5220/0006682803840391

Performance and energy-based cost prediction of virtual machines live migration in clouds

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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