Migration scheme based machine learning for QoS in cloud computing: Survey and research challenges

A. Young Son; Eui Nam Huh; Sang Ho Na; Pill Woo Lee

doi:10.1109/CAIPT.2017.8320748

Migration scheme based machine learning for QoS in cloud computing: Survey and research challenges

A. Young Son, Eui Nam Huh, Sang Ho Na, Pill Woo Lee

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

Abstract

VM migration has become a hot topic in the Cloud Dater Centers (CDCs). Numerous VM migration schemes are proposed for QoS aimed to improve various metrics affecting the CDCs. Also, it combines a Machine learning approach with modeling and predicting in CDC. Migration scheme through prediction based metric can greatly improve the physical machines resource utilization. Also, effective VM migration scheme can reduce the power consumption and time of the data centers. Thus, it needs to consider metrics which may impact the migration performance and energy efficiency. In this paper, we summarize and classify previous approaches of migration in CDCs. Furthermore, we conclude with a discussion of research problems in this area. In the future work, we will study on live migration mechanism to improve the live migration performance and energy efficiency in the variety of CDCs.

Original language	English
Title of host publication	Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017
Editors	Mukhlis Amien
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538606001
DOIs	https://doi.org/10.1109/CAIPT.2017.8320748
Publication status	Published - 2 Jul 2017
Event	4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017 - Kuta Bali, Indonesia Duration: 8 Aug 2017 → 10 Aug 2017

Publication series

Name	Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017
Volume	2018-January

Conference

Conference	4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017
Country/Territory	Indonesia
City	Kuta Bali
Period	8/08/17 → 10/08/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

cloud computing
energy efficiency
metric
migration
performance

UN SDGs

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

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10.1109/CAIPT.2017.8320748

Cite this

Son, A. Y., Huh, E. N., Na, S. H., & Lee, P. W. (2017). Migration scheme based machine learning for QoS in cloud computing: Survey and research challenges. In M. Amien (Ed.), Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017 (pp. 1-4). (Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAIPT.2017.8320748

Son, A. Young ; Huh, Eui Nam ; Na, Sang Ho et al. / Migration scheme based machine learning for QoS in cloud computing : Survey and research challenges. Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017. editor / Mukhlis Amien. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-4 (Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017).

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title = "Migration scheme based machine learning for QoS in cloud computing: Survey and research challenges",

abstract = "VM migration has become a hot topic in the Cloud Dater Centers (CDCs). Numerous VM migration schemes are proposed for QoS aimed to improve various metrics affecting the CDCs. Also, it combines a Machine learning approach with modeling and predicting in CDC. Migration scheme through prediction based metric can greatly improve the physical machines resource utilization. Also, effective VM migration scheme can reduce the power consumption and time of the data centers. Thus, it needs to consider metrics which may impact the migration performance and energy efficiency. In this paper, we summarize and classify previous approaches of migration in CDCs. Furthermore, we conclude with a discussion of research problems in this area. In the future work, we will study on live migration mechanism to improve the live migration performance and energy efficiency in the variety of CDCs.",

keywords = "cloud computing, energy efficiency, metric, migration, performance",

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Son, AY, Huh, EN, Na, SH & Lee, PW 2017, Migration scheme based machine learning for QoS in cloud computing: Survey and research challenges. in M Amien (ed.), Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017. Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017, Kuta Bali, Indonesia, 8/08/17. https://doi.org/10.1109/CAIPT.2017.8320748

Migration scheme based machine learning for QoS in cloud computing: Survey and research challenges. / Son, A. Young; Huh, Eui Nam; Na, Sang Ho et al.
Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017. ed. / Mukhlis Amien. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-4 (Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017; Vol. 2018-January).

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

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AB - VM migration has become a hot topic in the Cloud Dater Centers (CDCs). Numerous VM migration schemes are proposed for QoS aimed to improve various metrics affecting the CDCs. Also, it combines a Machine learning approach with modeling and predicting in CDC. Migration scheme through prediction based metric can greatly improve the physical machines resource utilization. Also, effective VM migration scheme can reduce the power consumption and time of the data centers. Thus, it needs to consider metrics which may impact the migration performance and energy efficiency. In this paper, we summarize and classify previous approaches of migration in CDCs. Furthermore, we conclude with a discussion of research problems in this area. In the future work, we will study on live migration mechanism to improve the live migration performance and energy efficiency in the variety of CDCs.

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Son AY, Huh EN, Na SH, Lee PW. Migration scheme based machine learning for QoS in cloud computing: Survey and research challenges. In Amien M, editor, Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-4. (Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017). doi: 10.1109/CAIPT.2017.8320748

Migration scheme based machine learning for QoS in cloud computing: Survey and research challenges

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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