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Bi-objective Heterogeneous Consolidation in Cloud Computing

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High Performance Computing (CARLA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 796))

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Abstract

In this paper, we address the problem of power-aware Virtual Machines (VMs) consolidation considering resource contention. Deployment of VMs can greatly influence host performance, especially, if they compete for resources on insufficient hardware. Performance can be drastically reduced and energy consumption increased. We focus on a bi-objective experimental evaluation of scheduling strategies for CPU and memory intensive jobs regarding the quality of service (QoS) and energy consumption objectives. We analyze energy consumption of the IBM System x3650 M4 server, with optimized performance for business-critical applications and cloud deployments built on IBM X-Architecture. We create power profiles for different types of applications and their combinations using SysBench benchmark. We evaluate algorithms with workload traces from Parallel Workloads and Grid Workload Archives and compare their non-dominated Pareto optimal solutions using set coverage and hyper volume metrics. Based on the presented case study, we show that our algorithms can provide the best energy and QoS trade-offs.

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Author information

Authors and Affiliations

  1. CICESE Research Center, Ensenada, Baja California, Mexico

    Luis-Angel Galaviz-Alejos, Fermín Armenta-Cano & Andrei Tchernykh

  2. South Ural State University, Chelyabinsk, Russia

    Gleb Radchenko

  3. Moscow Institute of Physics and Technology, State University, Moscow, Russia

    Alexander Yu. Drozdov

  4. Universidad Autónoma de Baja California, Mexicali, Baja California, Mexico

    Oleg Sergiyenko

  5. University of Göttingen, Göttingen, Germany

    Ramin Yahyapour

Authors
  1. Luis-Angel Galaviz-Alejos
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  2. Fermín Armenta-Cano
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  3. Andrei Tchernykh
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  4. Gleb Radchenko
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  5. Alexander Yu. Drozdov
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  6. Oleg Sergiyenko
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  7. Ramin Yahyapour
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Corresponding author

Correspondence to Andrei Tchernykh .

Editor information

Editors and Affiliations

  1. CSC-CONICET and Universidad de Buenos Aires, Buenos Aires, Argentina

    Esteban Mocskos

  2. Universidad de la República, Montevideo, Uruguay

    Sergio Nesmachnow

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Galaviz-Alejos, LA. et al. (2018). Bi-objective Heterogeneous Consolidation in Cloud Computing. In: Mocskos, E., Nesmachnow, S. (eds) High Performance Computing. CARLA 2017. Communications in Computer and Information Science, vol 796. Springer, Cham. https://doi.org/10.1007/978-3-319-73353-1_27

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  • DOI: https://doi.org/10.1007/978-3-319-73353-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73352-4

  • Online ISBN: 978-3-319-73353-1

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