Quality estimation of virtual machine placement in cloud infrastructures

Jorge López; Natalia Kushik; Djamal Zeghlache

doi:10.1007/978-3-319-67549-7_13

Quality estimation of virtual machine placement in cloud infrastructures

Jorge López, Natalia Kushik, Djamal Zeghlache

Université Paris-Saclay

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

Abstract

A virtual machine (VM) placement module is a component/part of a cloud (computing) infrastructure, which chooses the best host(s) to allocate the requested VMs. In the literature, skewed or biased criteria are often used to determine the correctness of a placement module. Therefore, the quality of existing placement solutions is not always assessed adequately. In this paper, we propose a distance function that estimates the quality of the placement by comparing it with an optimal solution. We show how this distance function is utilized for testing and monitoring the behavior of VM placement implementations. To validate our approach a simulator has been developed and used for estimating the quality of different placement modules running under various scenarios. Preliminary experimental results on VM placement algorithms implemented in widely used platforms, such as OpenStack show that very often VMs are placed very far from the optimal solutions.

Original language	English
Title of host publication	Testing Software and Systems - 29th IFIP WG 6.1 International Conference, ICTSS 2017, Proceedings
Editors	Nina Yevtushenko, Ana Rosa Cavalli, Husnu Yenigun
Publisher	Springer Verlag
Pages	213-229
Number of pages	17
ISBN (Print)	9783319675480
DOIs	https://doi.org/10.1007/978-3-319-67549-7_13
Publication status	Published - 1 Jan 2017
Externally published	Yes
Event	29th IFIP WG 6.1 International Conference on Testing Software and Systems, ICTSS 2017 - St. Petersburg, Russian Federation Duration: 9 Oct 2017 → 11 Oct 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10533 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	29th IFIP WG 6.1 International Conference on Testing Software and Systems, ICTSS 2017
Country/Territory	Russian Federation
City	St. Petersburg
Period	9/10/17 → 11/10/17

Keywords

Distance functions
Integer linear programming
Monitoring
Quality estimation
Testing
Virtual machine placement

Access to Document

10.1007/978-3-319-67549-7_13

Cite this

López, J., Kushik, N., & Zeghlache, D. (2017). Quality estimation of virtual machine placement in cloud infrastructures. In N. Yevtushenko, A. R. Cavalli, & H. Yenigun (Eds.), Testing Software and Systems - 29th IFIP WG 6.1 International Conference, ICTSS 2017, Proceedings (pp. 213-229). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10533 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67549-7_13

López, Jorge ; Kushik, Natalia ; Zeghlache, Djamal. / Quality estimation of virtual machine placement in cloud infrastructures. Testing Software and Systems - 29th IFIP WG 6.1 International Conference, ICTSS 2017, Proceedings. editor / Nina Yevtushenko ; Ana Rosa Cavalli ; Husnu Yenigun. Springer Verlag, 2017. pp. 213-229 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Quality estimation of virtual machine placement in cloud infrastructures",

abstract = "A virtual machine (VM) placement module is a component/part of a cloud (computing) infrastructure, which chooses the best host(s) to allocate the requested VMs. In the literature, skewed or biased criteria are often used to determine the correctness of a placement module. Therefore, the quality of existing placement solutions is not always assessed adequately. In this paper, we propose a distance function that estimates the quality of the placement by comparing it with an optimal solution. We show how this distance function is utilized for testing and monitoring the behavior of VM placement implementations. To validate our approach a simulator has been developed and used for estimating the quality of different placement modules running under various scenarios. Preliminary experimental results on VM placement algorithms implemented in widely used platforms, such as OpenStack show that very often VMs are placed very far from the optimal solutions.",

keywords = "Distance functions, Integer linear programming, Monitoring, Quality estimation, Testing, Virtual machine placement",

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doi = "10.1007/978-3-319-67549-7\_13",

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López, J, Kushik, N & Zeghlache, D 2017, Quality estimation of virtual machine placement in cloud infrastructures. in N Yevtushenko, AR Cavalli & H Yenigun (eds), Testing Software and Systems - 29th IFIP WG 6.1 International Conference, ICTSS 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10533 LNCS, Springer Verlag, pp. 213-229, 29th IFIP WG 6.1 International Conference on Testing Software and Systems, ICTSS 2017, St. Petersburg, Russian Federation, 9/10/17. https://doi.org/10.1007/978-3-319-67549-7_13

Quality estimation of virtual machine placement in cloud infrastructures. / López, Jorge; Kushik, Natalia ; Zeghlache, Djamal.
Testing Software and Systems - 29th IFIP WG 6.1 International Conference, ICTSS 2017, Proceedings. ed. / Nina Yevtushenko; Ana Rosa Cavalli; Husnu Yenigun. Springer Verlag, 2017. p. 213-229 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10533 LNCS).

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

TY - GEN

T1 - Quality estimation of virtual machine placement in cloud infrastructures

AU - López, Jorge

AU - Kushik, Natalia

AU - Zeghlache, Djamal

PY - 2017/1/1

Y1 - 2017/1/1

N2 - A virtual machine (VM) placement module is a component/part of a cloud (computing) infrastructure, which chooses the best host(s) to allocate the requested VMs. In the literature, skewed or biased criteria are often used to determine the correctness of a placement module. Therefore, the quality of existing placement solutions is not always assessed adequately. In this paper, we propose a distance function that estimates the quality of the placement by comparing it with an optimal solution. We show how this distance function is utilized for testing and monitoring the behavior of VM placement implementations. To validate our approach a simulator has been developed and used for estimating the quality of different placement modules running under various scenarios. Preliminary experimental results on VM placement algorithms implemented in widely used platforms, such as OpenStack show that very often VMs are placed very far from the optimal solutions.

AB - A virtual machine (VM) placement module is a component/part of a cloud (computing) infrastructure, which chooses the best host(s) to allocate the requested VMs. In the literature, skewed or biased criteria are often used to determine the correctness of a placement module. Therefore, the quality of existing placement solutions is not always assessed adequately. In this paper, we propose a distance function that estimates the quality of the placement by comparing it with an optimal solution. We show how this distance function is utilized for testing and monitoring the behavior of VM placement implementations. To validate our approach a simulator has been developed and used for estimating the quality of different placement modules running under various scenarios. Preliminary experimental results on VM placement algorithms implemented in widely used platforms, such as OpenStack show that very often VMs are placed very far from the optimal solutions.

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KW - Integer linear programming

KW - Monitoring

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KW - Virtual machine placement

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DO - 10.1007/978-3-319-67549-7_13

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AN - SCOPUS:85030701407

SN - 9783319675480

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

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BT - Testing Software and Systems - 29th IFIP WG 6.1 International Conference, ICTSS 2017, Proceedings

A2 - Yevtushenko, Nina

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A2 - Yenigun, Husnu

PB - Springer Verlag

T2 - 29th IFIP WG 6.1 International Conference on Testing Software and Systems, ICTSS 2017

Y2 - 9 October 2017 through 11 October 2017

ER -

López J, Kushik N , Zeghlache D. Quality estimation of virtual machine placement in cloud infrastructures. In Yevtushenko N, Cavalli AR, Yenigun H, editors, Testing Software and Systems - 29th IFIP WG 6.1 International Conference, ICTSS 2017, Proceedings. Springer Verlag. 2017. p. 213-229. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67549-7_13

Quality estimation of virtual machine placement in cloud infrastructures

Abstract

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Conference

Keywords

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