An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification

Rachael Shaw; Enda Howley; Enda Barrett

doi:10.1007/978-3-030-33702-5_20

An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification

Rachael Shaw, Enda Howley, Enda Barrett

Computer Science

University of Galway

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

5 Citations (Scopus)

Abstract

Inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Virtual Machine (VM) consolidation is an effective method for improving energy related costs and environmental sustainability for modern data centers. While dynamic VM consolidation can improve energy efficiency, virtualisation technologies cannot guarantee performance isolation between co-located VMs resulting in interference issues. We address the problem by introducing a energy and interference aware VM consolidation algorithm. The proposed algorithm utilizes the predictive capabilities of a Machine Learning (ML) model in an attempt to classify VM workloads to make more informed consolidation decisions. Furthermore, using recent workload data from Microsoft Azure we present a comparative study of two popular classification algorithms and select the model with the best performance to incorporate into our proposed approach. Our empirical results demonstrate how our approach improves energy efficiency by 31% while also reducing service violations by 69%.

Original language	English
Title of host publication	Service-Oriented Computing - 17th International Conference, ICSOC 2019, Proceedings
Editors	Sami Yangui, Khalil Drira, Ismael Bouassida Rodriguez, Zahir Tari
Publisher	Springer
Pages	251-266
Number of pages	16
ISBN (Print)	9783030337018
DOIs	https://doi.org/10.1007/978-3-030-33702-5_20
Publication status	Published - 2019
Event	17th International Conference on Service-Oriented Computing, ICSOC 2019 - Toulouse, France Duration: 28 Oct 2019 → 31 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11895 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Service-Oriented Computing, ICSOC 2019
Country/Territory	France
City	Toulouse
Period	28/10/19 → 31/10/19

Keywords

Classification
Energy efficiency
Interference aware
Machine Learning
Virtual machine consolidation

UN SDGs

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

Access to Document

10.1007/978-3-030-33702-5_20

Cite this

Shaw, R., Howley, E., & Barrett, E. (2019). An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification. In S. Yangui, K. Drira, I. Bouassida Rodriguez, & Z. Tari (Eds.), Service-Oriented Computing - 17th International Conference, ICSOC 2019, Proceedings (pp. 251-266). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11895 LNCS). Springer . https://doi.org/10.1007/978-3-030-33702-5_20

Shaw, Rachael ; Howley, Enda ; Barrett, Enda. / An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification. Service-Oriented Computing - 17th International Conference, ICSOC 2019, Proceedings. editor / Sami Yangui ; Khalil Drira ; Ismael Bouassida Rodriguez ; Zahir Tari. Springer , 2019. pp. 251-266 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification",

abstract = "Inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Virtual Machine (VM) consolidation is an effective method for improving energy related costs and environmental sustainability for modern data centers. While dynamic VM consolidation can improve energy efficiency, virtualisation technologies cannot guarantee performance isolation between co-located VMs resulting in interference issues. We address the problem by introducing a energy and interference aware VM consolidation algorithm. The proposed algorithm utilizes the predictive capabilities of a Machine Learning (ML) model in an attempt to classify VM workloads to make more informed consolidation decisions. Furthermore, using recent workload data from Microsoft Azure we present a comparative study of two popular classification algorithms and select the model with the best performance to incorporate into our proposed approach. Our empirical results demonstrate how our approach improves energy efficiency by 31\% while also reducing service violations by 69\%.",

keywords = "Classification, Energy efficiency, Interference aware, Machine Learning, Virtual machine consolidation",

author = "Rachael Shaw and Enda Howley and Enda Barrett",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 17th International Conference on Service-Oriented Computing, ICSOC 2019 ; Conference date: 28-10-2019 Through 31-10-2019",

year = "2019",

doi = "10.1007/978-3-030-33702-5\_20",

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isbn = "9783030337018",

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Shaw, R, Howley, E & Barrett, E 2019, An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification. in S Yangui, K Drira, I Bouassida Rodriguez & Z Tari (eds), Service-Oriented Computing - 17th International Conference, ICSOC 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11895 LNCS, Springer , pp. 251-266, 17th International Conference on Service-Oriented Computing, ICSOC 2019, Toulouse, France, 28/10/19. https://doi.org/10.1007/978-3-030-33702-5_20

An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification. / Shaw, Rachael; Howley, Enda ; Barrett, Enda.
Service-Oriented Computing - 17th International Conference, ICSOC 2019, Proceedings. ed. / Sami Yangui; Khalil Drira; Ismael Bouassida Rodriguez; Zahir Tari. Springer , 2019. p. 251-266 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11895 LNCS).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification

AU - Shaw, Rachael

AU - Howley, Enda

AU - Barrett, Enda

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Virtual Machine (VM) consolidation is an effective method for improving energy related costs and environmental sustainability for modern data centers. While dynamic VM consolidation can improve energy efficiency, virtualisation technologies cannot guarantee performance isolation between co-located VMs resulting in interference issues. We address the problem by introducing a energy and interference aware VM consolidation algorithm. The proposed algorithm utilizes the predictive capabilities of a Machine Learning (ML) model in an attempt to classify VM workloads to make more informed consolidation decisions. Furthermore, using recent workload data from Microsoft Azure we present a comparative study of two popular classification algorithms and select the model with the best performance to incorporate into our proposed approach. Our empirical results demonstrate how our approach improves energy efficiency by 31% while also reducing service violations by 69%.

AB - Inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Virtual Machine (VM) consolidation is an effective method for improving energy related costs and environmental sustainability for modern data centers. While dynamic VM consolidation can improve energy efficiency, virtualisation technologies cannot guarantee performance isolation between co-located VMs resulting in interference issues. We address the problem by introducing a energy and interference aware VM consolidation algorithm. The proposed algorithm utilizes the predictive capabilities of a Machine Learning (ML) model in an attempt to classify VM workloads to make more informed consolidation decisions. Furthermore, using recent workload data from Microsoft Azure we present a comparative study of two popular classification algorithms and select the model with the best performance to incorporate into our proposed approach. Our empirical results demonstrate how our approach improves energy efficiency by 31% while also reducing service violations by 69%.

KW - Classification

KW - Energy efficiency

KW - Interference aware

KW - Machine Learning

KW - Virtual machine consolidation

UR - https://www.scopus.com/pages/publications/85073216707

U2 - 10.1007/978-3-030-33702-5_20

DO - 10.1007/978-3-030-33702-5_20

M3 - Conference Publication

AN - SCOPUS:85073216707

SN - 9783030337018

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

SP - 251

EP - 266

BT - Service-Oriented Computing - 17th International Conference, ICSOC 2019, Proceedings

A2 - Yangui, Sami

A2 - Drira, Khalil

A2 - Bouassida Rodriguez, Ismael

A2 - Tari, Zahir

PB - Springer

T2 - 17th International Conference on Service-Oriented Computing, ICSOC 2019

Y2 - 28 October 2019 through 31 October 2019

ER -

Shaw R, Howley E , Barrett E. An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification. In Yangui S, Drira K, Bouassida Rodriguez I, Tari Z, editors, Service-Oriented Computing - 17th International Conference, ICSOC 2019, Proceedings. Springer . 2019. p. 251-266. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-33702-5_20

An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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