A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers

Ennio Torre; Juan J. Durillo; Vincenzo de Maio; Prateek Agrawal; Shajulin Benedict; Nishant Saurabh; Radu Prodan

doi:10.1016/j.infsof.2020.106390

A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers

Ennio Torre, Juan J. Durillo, Vincenzo de Maio, Prateek Agrawal, Shajulin Benedict, Nishant Saurabh, Radu Prodan^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.

Original language	English
Article number	106390
Journal	Information and Software Technology
Volume	128
DOIs	https://doi.org/10.1016/j.infsof.2020.106390
Publication status	Published - Dec 2020
Externally published	Yes

Bibliographical note

Funding Information:
This work received funding from: European Union's Horizon 2020 research and innovation programme, grant agreement 825134, ?Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)?; Austrian Science Fund (FWF), grant agreement Y 904 START-Programm 2015, ?Runtime Control in Multi Clouds (RUCON)?; Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, ?Energy Aware Workflow Compiler for Future Heterogeneous Systems?.

Funding Information:
This work received funding from: European Union’s Horizon 2020 research and innovation programme, grant agreement 825134 , “Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)”; Austrian Science Fund (FWF), grant agreement Y 904 START-Programm 2015, “Runtime Control in Multi Clouds (RUCON)”; Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, “Energy Aware Workflow Compiler for Future Heterogeneous Systems”.

Publisher Copyright:
© 2020

Funding

This work received funding from: European Union's Horizon 2020 research and innovation programme, grant agreement 825134, ?Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)?; Austrian Science Fund (FWF), grant agreement Y 904 START-Programm 2015, ?Runtime Control in Multi Clouds (RUCON)?; Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, ?Energy Aware Workflow Compiler for Future Heterogeneous Systems?. This work received funding from: European Union’s Horizon 2020 research and innovation programme, grant agreement 825134 , “Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)”; Austrian Science Fund (FWF), grant agreement Y 904 START-Programm 2015, “Runtime Control in Multi Clouds (RUCON)”; Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, “Energy Aware Workflow Compiler for Future Heterogeneous Systems”.

Keywords

Data center simulation
Energy consumption
Genetic algorithm
Live migration
Multi-objective optimisation
Pareto optimal set
Resource overcommitment
Resource wastage
VM placement

UN SDGs

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

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10.1016/j.infsof.2020.106390Licence: CC BY

Cite this

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title = "A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers",

abstract = "Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.",

keywords = "Data center simulation, Energy consumption, Genetic algorithm, Live migration, Multi-objective optimisation, Pareto optimal set, Resource overcommitment, Resource wastage, VM placement",

author = "Ennio Torre and Durillo, {Juan J.} and {de Maio}, Vincenzo and Prateek Agrawal and Shajulin Benedict and Nishant Saurabh and Radu Prodan",

note = "Funding Information: This work received funding from: European Union's Horizon 2020 research and innovation programme, grant agreement 825134, ?Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)?; Austrian Science Fund (FWF), grant agreement Y 904 START-Programm 2015, ?Runtime Control in Multi Clouds (RUCON)?; Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, ?Energy Aware Workflow Compiler for Future Heterogeneous Systems?. Funding Information: This work received funding from: European Union{\textquoteright}s Horizon 2020 research and innovation programme, grant agreement 825134 , “Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)”; Austrian Science Fund (FWF), grant agreement Y 904 START-Programm 2015, “Runtime Control in Multi Clouds (RUCON)”; Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, “Energy Aware Workflow Compiler for Future Heterogeneous Systems”. Publisher Copyright: {\textcopyright} 2020",

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AU - Durillo, Juan J.

AU - de Maio, Vincenzo

AU - Agrawal, Prateek

AU - Benedict, Shajulin

AU - Saurabh, Nishant

AU - Prodan, Radu

N1 - Funding Information: This work received funding from: European Union's Horizon 2020 research and innovation programme, grant agreement 825134, ?Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)?; Austrian Science Fund (FWF), grant agreement Y 904 START-Programm 2015, ?Runtime Control in Multi Clouds (RUCON)?; Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, ?Energy Aware Workflow Compiler for Future Heterogeneous Systems?. Funding Information: This work received funding from: European Union’s Horizon 2020 research and innovation programme, grant agreement 825134 , “Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)”; Austrian Science Fund (FWF), grant agreement Y 904 START-Programm 2015, “Runtime Control in Multi Clouds (RUCON)”; Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, “Energy Aware Workflow Compiler for Future Heterogeneous Systems”. Publisher Copyright: © 2020

PY - 2020/12

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N2 - Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.

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KW - Pareto optimal set

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KW - Resource wastage

KW - VM placement

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JF - Information and Software Technology

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ER -

A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

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