More Web Proxy on the site http://driver.im/

research-article

An energy aware framework for virtual machine placement in cloud federated data centres

Authors:

Corentin Dupont,

Thomas Schulze,

Giovanni Giuliani,

Fabien HermenierAuthors Info & Claims

e-Energy '12: Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet

Article No.: 4, Pages 1 - 10

https://doi.org/10.1145/2208828.2208832

Published: 09 May 2012 Publication History

Abstract

Data centres are powerful ICT facilities which constantly evolve in size, complexity, and power consumption. At the same time users' and operators' requirements become more and more complex. However, existing data centre frameworks do not typically take energy consumption into account as a key parameter of the data centre's configuration. To lower the power consumption while fulfilling performance requirements we propose a flexible and energy-aware framework for the (re)allocation of virtual machines in a data centre. The framework, being independent from the data centre management system, computes and enacts the best possible placement of virtual machines based on constraints expressed through service level agreements. The framework's flexibility is achieved by decoupling the expressed constraints from the algorithms using the Constraint Programming (CP) paradigm and programming language, basing ourselves on a cluster management library called Entropy. Finally, the experimental and simulation results demonstrate the effectiveness of this approach in achieving the pursued energy optimization goals.

References

[1]

Berral, J. L., Goiri, I., Nou, R., Julia, F., Guitart, J., Gavalda, R., and Torres, J. 2010. Towards energy-aware scheduling in data centers using machine learning. In Proceedings of the 1^st International Conference on Energy-Efficient Computing and Networking (Passau, Germany, April 13--15, 2010). e-Energy'10. ACM, New York, NY 215--224. DOI=10.1145/1791314.1791349

Digital Library

[2]

Green Grid Consortium, http://www.thegreengrid.org

[3]

Banerjee, A., Mukherjee, T., Varsamopoulos, G., Gupta, S. K. S. 2010. Cooling-aware and thermal-aware workload placement for green HPC data centers. In Proceedings of International Green Computing Conference(Chicago, IL, USA, August 15--18, 2010). 245--256. DOI=10.1109/DREENCOMP.2010.5598306.

Digital Library

[4]

Pakbaznia, E. and Pedram, M. 2009. Minimizing data center cooling and server power costs. In Proceedings of the 14th ACM/IEEE International Symposium on Low Power Electronics and Design (San Francisco, CA, USA, August 19--21). ISPLED'09. ACM, New York, NY 145--150. DOI = http://doi.acm.org/10.1145/1594233.1594268

Digital Library

[5]

Meisner, D., Gold, B. T., and Wenisch, T. F. 2009. PowerNap: Eliminating server idle power. In proceedings of the 14^th International Conference on Architectural Support for Programming Languages and Operating Systems (Washington, DC, USA, March 7--11, 2009). ASPLOS'09. ACM, New York, NY 205--216. DOI = 10.1145/1508244.1508269

Digital Library

[6]

Carrol, R., Balasubramaniam, S., Donnelly, W., and D. Botvich. 2011. Dynamic optimization solution for green service migration in data centres. In Proceedings of IEEE International Conference on Communications (Kyoto, Japan, June 5--9, 2011). ICC'11.pp. 1--6, 2011. DOI = 10.1109/icc.2011.5963030.

[7]

Garg, S. K., Yeo, C. S., Anandasivam, A., and Buyya, R. 2010. Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. Journal of Parallel and Distributed Computing. 71 (2010), 732--749.

Digital Library

[8]

Barbagallo, D., Nitto, E., Dubois, D. J., and Mirandola, R. 2010. A Bio-inspired algorithm for energy optimization in a self-organizing data center. In Proceedings of the FirstSelf-organizing architectures (Cambridge, UK). SOAR'09. Springer-Verlag Berlin, Heidelberg, 127--151. ISBN:3-642-14411-X 978-3-642-14411-0.

Digital Library

[9]

Paschke, A., Schnappinger-Gerull, E. 2006. A categorization scheme for SLA metrics. In Proceedings of Service Oriented Electronic Commerce, Vol 80, p. 25--40

[10]

Bistarelli, S., Santini, F. 2008. A nonmonologic soft concurrent constraint language for sla negotiation, Proc. CILC'08

[11]

Buscemi, M., Montanari, U. 2007. Cc-pi: A constraint-based language for specifying service level agreements. In Proceedings of the 16^th European conference on programming. (Braga, Portugal). ESOP'07. Springer Verlag Berlin, Heidelberg 18--32. ISBN: 978-3-540-71314-2

Digital Library

[12]

Klingert, S., Schulze, T., Bunse, C. 2011. GreenSLAs for the Energy-effcient Management of Data Centres. In Proceedings of the Second International Conference on Energy-effcient Computing and Networking (New York, USA, May 31-June 1, 2011). e-Energy'11. ACM, New York, NY.

Digital Library

[13]

Hermenier, F., Demassey, S., Lorca, X. 2011. Bin repacking scheduling in virtualized datacenters. In Proceedings of the 17th International Conference on Principles and Practice of Constraint Programming (Perugia, Italy). CP'11. Jimmy Lee (Ed.). Springer-Verlag, Berlin, Heidelberg, 27--41.

Digital Library

[14]

Chen, Y., Iyer, S., Liu, X., Milojicic, D., Sahai, A. 2007. SLA decomposition: Translating service level objectives to system level thresholds. In Proceedings of the Fourth International Conference on Autonomic Computing (Washington, DC, USA, 2007). ICAC'07. IEEE Computer Society. DOI=10.1109/ICAC.2007.36.

Digital Library

[15]

Quan, D.-M., Basmadjian, R., De Meer, H., Lent, R., Mahmoodi, T., Sannelli, D., Mezza, F., Dupont, C. 2011. Energy efficient resource allocation strategy for cloud data centres. In Proceedings of the 26^th International Symposium on Computer and information Sciences (London, UK, September 26-28, 2011). ISCIS'11. Springer, 133--141.

[16]

Lawler, E. 1983. Recent results in the theory of machine scheduling. In Mathematical Programming: The State of the Art. Springer-Verlag, Berlin, Germany.

[17]

N. Bobroff, A. Kochut, and K. Beaty. Dynamic placement of virtual machines for managing SLA violations. Integrated Network Management, 2007. IM '07. 10th IFIP/IEEE International Symposium on, pages 119--128, May 2007.

[18]

Wood, T., Shenoy, P. J., Venkataramani, A. Yousif, M. S. 2007. Black-box and gray-box strategies for virtual machine migration. In Proceedings of the 4th ACM/USENIX Symposium on Networked Systems Design and Implementation (Cambridge, MA, USA). NSDI '07. USENIX Association, Berkeley, CA, USA,17--17.

Digital Library

[19]

Verma, A., Ahuja, P., Neogi, A. 2008. Power-aware dynamic placement of hpc applications. In Proceedings of the 22nd Annual International Conference on Supercomputing(Island of Kos, Greece). ICS '08. ACM, New York, NY, 175--184. DOI=10.1145/1375527.1375555.

Digital Library

[20]

Dhyani, K., Gualandi, S., Cremonesi, P. 2010. A Constraint programming approach for the service consolidation problem. In Lecture Notes of Computer Science, 6140(2010). Springer, 97--101. DOI=10.1007/978-3-642-13520-0-13.

Digital Library

[21]

Anderson, E., Hall, J., Hartline, J., Hobbes, M., Karlin, A., Saia, J., Swaminathan, R., Wilkes, J. 2010. Algorithms for data migration. J. Algorithmica, 57(2), 349--380.

Digital Library

[22]

Fukunaga, A. 2009. Search spaces for min-perturbation repair. In Proceedings of the 15th International Conference on Principles and Practice of Constraint Programming (Lisbon, Portugal). CP'09. Springer Verlag Berlin, Heidelberg, 383--390.

Digital Library

[23]

FIT4Green EU Project, http://www.fit4green.eu

[24]

Rossi, F., Van Beek, P., Walsh, T. 2006. Handbook of Constraints Programming. Elsevier Science Inc.

Digital Library

[25]

Choco, http://choco.emn.fr

Cited By

Karthikeyan RSundaravadivazhagan BCyriac RBalachandran PShitharth S(2023)Preserving Resource Handiness and Exigency-Based Migration Algorithm (PRH-EM) for Energy Efficient Federated Cloud Management SystemsMobile Information Systems10.1155/2023/77547652023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/7754765
Radi MAlwan AGulzar Y(2023)Genetic-Based Virtual Machines Consolidation Strategy With Efficient Energy Consumption in Cloud EnvironmentIEEE Access10.1109/ACCESS.2023.327629211(48022-48032)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3276292
Khodayarseresht EShameli-Sendi A(2023)A multi-objective cloud energy optimizer algorithm for federated environmentsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.12.007174(81-99)Online publication date: Apr-2023
https://doi.org/10.1016/j.jpdc.2022.12.007
Show More Cited By

Index Terms

An energy aware framework for virtual machine placement in cloud federated data centres
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

Recommendations

A joint CPU-RAM energy efficient and SLA-compliant approach for cloud data centers

Cloud computing is a new paradigm that offers computing resources in a virtualized way with unprecedented levels of flexibility, reliability, and scalability. The benefits of cloud computing, however, come at a high cost in terms of energy consumption, ...
Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

Cloud computing offers utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications ...
Energy-efficient virtual machine provisioning mechanism in cloud computing environments
PCI '15: Proceedings of the 19th Panhellenic Conference on Informatics

As the increasing number of modern applications and enterprises demand more and more resources in computational power, memory and disk storage, cloud data centers are consuming huge amounts of electrical energy. The aim of cloud service providers is to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

e-Energy '12: Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet

May 2012

250 pages

ISBN:9781450310550

DOI:10.1145/2208828

General Chairs:
Marco Ajmone Marsan
Politecnico di Torino, Italy, and Institute IMDEA Networks, Spain
,
Suresh Goyal
Alcatel-Lucent Bell Labs
,
Shugong Xu
Huawei Technologies, China
,
Program Chairs:
Antonio Fernández
Anta Institute IMDEA Networks, Spain
,
Milan Prodanovic
Institute IMDEA Energy, Spain
,
Ken Christensen
University of South Florida

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

IEEE-CS\DATC: IEEE Computer Society

In-Cooperation

SIGCOMM: ACM Special Interest Group on Data Communication

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 May 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

e-Energy'12

Sponsor:

IEEE-CS\DATC

e-Energy'12: 3rd International Conference on Energy-Efficient Computing and Networking

May 9 - 11, 2012

Madrid, Spain

Acceptance Rates

Overall Acceptance Rate 160 of 446 submissions, 36%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

72
Total Citations
View Citations
489
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)1

Reflects downloads up to 10 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Karthikeyan RSundaravadivazhagan BCyriac RBalachandran PShitharth S(2023)Preserving Resource Handiness and Exigency-Based Migration Algorithm (PRH-EM) for Energy Efficient Federated Cloud Management SystemsMobile Information Systems10.1155/2023/77547652023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/7754765
Radi MAlwan AGulzar Y(2023)Genetic-Based Virtual Machines Consolidation Strategy With Efficient Energy Consumption in Cloud EnvironmentIEEE Access10.1109/ACCESS.2023.327629211(48022-48032)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3276292
Khodayarseresht EShameli-Sendi A(2023)A multi-objective cloud energy optimizer algorithm for federated environmentsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.12.007174(81-99)Online publication date: Apr-2023
https://doi.org/10.1016/j.jpdc.2022.12.007
Patni SSingh A(2023)An Optimal Host Allocation and Load Distribution Framework Using Maximum Likelihood in Cloud EnvironmentSN Computer Science10.1007/s42979-023-01939-24:5Online publication date: 29-Jul-2023
https://doi.org/10.1007/s42979-023-01939-2
Li NLiu XWang YMojarad M(2023)Improving Dynamic Placement of Virtual Machines in Cloud Data Centers Based on Open-Source Development Model AlgorithmJournal of Grid Computing10.1007/s10723-023-09651-421:1Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1007/s10723-023-09651-4
Magotra BMalhotra D(2022)Resource-Efficient VM Placement in the Cloud Environment Using Improved Particle Swarm OptimizationInternational Journal of Applied Metaheuristic Computing10.4018/IJAMC.29831213:1(1-32)Online publication date: 1-Jan-2022
https://doi.org/10.4018/IJAMC.298312
Jayakumar LChitra RSivasankari JVidhya SAlimzhanova LKazbekova GKulambayev BKostangeldinova ADevi STeressa D(2022)QoS Analysis for Cloud-Based IoT Data Using Multicriteria-Based Optimization ApproachComputational Intelligence and Neuroscience10.1155/2022/72559132022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7255913
Supreeth SPatil KPatil SRohith S(2022)Comparative approach for VM Scheduling using Modified Particle Swarm Optimization and Genetic Algorithm in Cloud Computing2022 IEEE International Conference on Data Science and Information System (ICDSIS)10.1109/ICDSIS55133.2022.9915907(1-6)Online publication date: 29-Jul-2022
https://doi.org/10.1109/ICDSIS55133.2022.9915907
Magotra BMalhotra DDogra A(2022)Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM ConsolidationArchives of Computational Methods in Engineering10.1007/s11831-022-09852-230:3(1789-1818)Online publication date: 27-Nov-2022
https://doi.org/10.1007/s11831-022-09852-2
Yan JXiao JHong X(2021)Dueling-DDQN Based Virtual Machine Placement Algorithm for Cloud Computing Systems2021 IEEE/CIC International Conference on Communications in China (ICCC)10.1109/ICCC52777.2021.9580393(294-299)Online publication date: 28-Jul-2021
https://doi.org/10.1109/ICCC52777.2021.9580393
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents