research-article

Challenge: Resolving Data Center Power Bill Disputes: The Energy-Performance Trade-Offs of Consolidation

Authors:

Angelos Chatzipapas,

Dimosthenis Pediaditakis,

Charalampos Rotsos,

Vincenzo Mancuso,

Andrew MooreAuthors Info & Claims

e-Energy '15: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems

Pages 89 - 94

https://doi.org/10.1145/2768510.2770933

Published: 14 July 2015 Publication History

Abstract

In this paper we challenge the common evaluation practices used in Virtual Machine (VM) consolidation, such as simulation and small testbeds, which fail to capture the fundamental trade-off between energy consumption and performance. We identify a number of over-simplifying assumptions which are typically made about the energy consumption and performance characteristics of modern networked systems. In response, we describe how more accurate models for data-center systems can be designed and used in order to create an evaluation framework that allows the exploration of the energy-performance trade-off in VM consolidation strategies with enhanced fidelity.

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Cited By

Seyyedsalehi SKhansari M(2022)Virtual Machine Placement Optimization for Big Data Applications in Cloud ComputingIEEE Access10.1109/ACCESS.2022.320305710(96112-96127)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3203057
Yao CLiu WTang WHu S(2022)EAISFuture Generation Computer Systems10.1016/j.future.2022.01.004130:C(253-268)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.future.2022.01.004
Agarwal ASun JNoghabi SIyengar SBadam AChandra RSeshan SKalyanaraman S(2021)Redesigning Data Centers for Renewable EnergyProceedings of the 20th ACM Workshop on Hot Topics in Networks10.1145/3484266.3487394(45-52)Online publication date: 10-Nov-2021
https://dl.acm.org/doi/10.1145/3484266.3487394
Show More Cited By

Index Terms

Challenge: Resolving Data Center Power Bill Disputes: The Energy-Performance Trade-Offs of Consolidation
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Published In

cover image ACM Conferences

e-Energy '15: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems

July 2015

334 pages

ISBN:9781450336093

DOI:10.1145/2768510

General Chairs:
Shivkumar Kalyanaraman
IBM Research, India
,
Deva P. Seetharam
DataGlen Technologies, India
,
Rajeev Shorey
TCS Innovation Labs, USA/India
,
Program Chairs:
Sarvapali Ramchurn
University of Southampton, UK
,
Mani Srivastava
UCLA, USA

Copyright © 2015 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 July 2015

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Defense Advanced Research Projects Agency (DARPA) / Air Force Research Labo- ratory (AFRL)
Greek State Scholarship Foundation
EPSRC TOUCAN project
EPSRC INTERNET Project
MINECO

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e-Energy'15

Sponsor:

SIGCOMM

e-Energy'15: The Sixth International Conference on Future Energy Systems

July 14 - 17, 2015

Bangalore, India

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e-Energy '15 Paper Acceptance Rate 20 of 85 submissions, 24%;

Overall Acceptance Rate 160 of 446 submissions, 36%

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E-Energy '25

Sponsor:
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The 16th ACM International Conference on Future and Sustainable Energy Systems

June 17 - 20, 2025

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Cited By

Seyyedsalehi SKhansari M(2022)Virtual Machine Placement Optimization for Big Data Applications in Cloud ComputingIEEE Access10.1109/ACCESS.2022.320305710(96112-96127)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3203057
Yao CLiu WTang WHu S(2022)EAISFuture Generation Computer Systems10.1016/j.future.2022.01.004130:C(253-268)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.future.2022.01.004
Agarwal ASun JNoghabi SIyengar SBadam AChandra RSeshan SKalyanaraman S(2021)Redesigning Data Centers for Renewable EnergyProceedings of the 20th ACM Workshop on Hot Topics in Networks10.1145/3484266.3487394(45-52)Online publication date: 10-Nov-2021
https://dl.acm.org/doi/10.1145/3484266.3487394
Ahmed KBollen MAlvarez M(2021)A Review of Data Centers Energy Consumption and Reliability ModelingIEEE Access10.1109/ACCESS.2021.31250929(152536-152563)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3125092
Jin CBai XYang CMao WXu X(2020)A review of power consumption models of servers in data centersApplied Energy10.1016/j.apenergy.2020.114806265(114806)Online publication date: May-2020
https://doi.org/10.1016/j.apenergy.2020.114806
DIOUANI SMEDROMI H(2019)How energy consumption in the cloud data center is calculated2019 International Conference of Computer Science and Renewable Energies (ICCSRE)10.1109/ICCSRE.2019.8807458(1-10)Online publication date: Jul-2019
https://doi.org/10.1109/ICCSRE.2019.8807458
Dayarathna MWen YFan R(2016)Data Center Energy Consumption Modeling: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2015.248118318:1(732-794)Online publication date: Sep-2017
https://doi.org/10.1109/COMST.2015.2481183

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