research-article

Improving Energy Efficiency for Transactional Workloads in Cloud Environments

Authors:

B. PerniciAuthors Info & Claims

e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems

Pages 290 - 295

https://doi.org/10.1145/3077839.3084026

Published: 16 May 2017 Publication History

Abstract

Research on energy efficiency in data centers has been focusing on reducing energy consumption, and state-of-the-art techniques have been emphasizing on optimizing power and energy consumption at hardware and infrastructure levels of data centers. Although these techniques have achieved significant improvement in reducing the energy consumption of data centers, the increasing heterogeneity of the current workloads call for more holistic approaches to enable optimization at higher levels. The goal of this work is to look for new opportunities to further improve energy efficiency at the level of applications with a focus on transactional workloads. In particular, we propose the model to characterize the energy per job of transactional-based applications. The model is experimentally validated on a real federated cloud infrastructure. Alternative policies to optimize the energy consumption of transactional applications are evaluated on the basis of the model.

References

[1]

Victor Avelar, Dan Azevedo, Alan French, and Emerson Network Power. 2012. PUE: a comprehensive examination of the metric. White paper 49 (2012).

[2]

Christian Belady, Andy Rawson, John Pfleuger, and Tahir Cader. 2008. The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE. Technical Report. Technical report, Green Grid.

[3]

Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, and Albert Zomaya. 2011. A taxonomy and survey of energy-efficient data centers and cloud computing systems. Advances in Computers 82, 2 (2011), 47--111.

[4]

Cinzia Cappiello, Nguyen Thi Thao Ho, Barbara Pernici, Pierluigi Plebani, and Monica Vitali. 2016. CO2-Aware Adaptation Strategies for Cloud Applications. IEEE Transactions on Cloud Computing 4, 2 (April 2016), 152--165.

[5]

D. Cerotti, M. Gribaudo, P. Piazzolla, R. Pinciroli, and G. Serazzi. 2014. Multiclass queuing networks models for energy optimization. Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2014 (2014), 98--105. cited By 1.

Digital Library

[6]

D. Cerotti, M. Gribaudo, P. Piazzolla, R. Pinciroli, and G. Serazzi. 2015. Modeling power consumption in multicore CPUs with multithreading and frequency scaling. Lecture Notes in Electrical Engineering 355 (2015), 81--90. cited By 0.

[7]

Tudor Cioara, Ionut Anghel, Ioan Salomie, Georgiana Copil, Daniel Moldovan, and Alexander Kipp. 2011. Energy aware dynamic resource consolidation algorithm for virtualized service centers based on reinforcement learning. In 201110th International Symposium on Parallel and Distributed Computing. IEEE, 163--169.

Digital Library

[8]

Green IT Promotion Council. 2010. Introduction of Datacenter Performance per Energy. http://home.jeita.or.jp/greenitpc/activity/symposium/110223/pdf/ss6_nttdata.pdf. (2010).

[9]

Miyuru Dayarathna, Yonggang Wen, and Rui Fan. 2016. Data Center Energy Consumption Modeling: A Survey. IEEE Communications Surveys & Tutorials 18, 1 (2016), 732--794.

Digital Library

[10]

Xiaobo Fan, Wolf-Dietrich Weber, and Luiz Andre Barroso. 2007. Power provisioning for a warehouse-sized computer. In ACM SIGARCH Computer Architecture News, Vol. 35. ACM, 13--23.

Digital Library

[11]

Eugen Feller, Louis Rilling, and Christine Morin. 2011. Energy-aware ant colony based workload placement in clouds. In Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing. IEEE Computer Society, 26--33.

Digital Library

[12]

Anshul Gandhi, Varun Gupta, Mor Harchol-Balter, and Michael A Kozuch. 2010. Optimality analysis of energy-performance trade-off for server farm management. Performance Evaluation 67, 11 (2010), 1155--1171.

Digital Library

[13]

Anshul Gandhi, Mor Harchol-Balter, and Ivo Adan. 2010. Server farms with setup costs. Performance Evaluation 67, 11 (2010), 1123--1138.

Digital Library

[14]

Erol Gelenbe and Ricardo Lent. 2012. Trade-offs between energy and quality of service. In Sustainable Internet and ICT for Sustainability (SustainIT), 2012. IEEE, 1--5.

[15]

Marco Gribaudo, Thi Thao Nguyen Ho, Barbara Pernici, and Giuseppe Serazzi. 2014. Analysis of the influence of application deployment on energy consumption. In International Workshop on Energy Efficient Data Centers. Springer, 87--101.

[16]

Thi Thao Nguyen Ho, Marco Gribaudo, and Barbara Pernici. 2016. Characterizing Energy per Job in Cloud Applications. Electronics 5, 4 (2016). http://www.mdpi.com/2079-9292/5/4/90

[17]

Aman Kansal, Feng Zhao, Jie Liu, Nupur Kothari, and Arka A Bhattacharya. 2010. Virtual machine power metering and provisioning. In Proceedings of the 1st ACM Symposium on Cloud Computing. ACM, 39--50.

Digital Library

[18]

Yacine Kessaci, Mohand Mezmaz, Nouredine Melab, El-Ghazali Talbi, and Daniel Tuyttens. 2011. Parallel evolutionary algorithms for energy aware scheduling. In Intelligent Decision Systems in Large-Scale Distributed Environments. Springer, 75--100.

[19]

Alexander Kipp, Tao Jiang, Jia Liu, Mariagrazia Fugini, Monica Vitali, Barbara Pernici, and Ioan Salomie. 2012. Applying green metrics to optimise the energy-consumption footprint of IT service centres. International Journal of Space-Based and Situated Computing 5 2, 3 (2012), 158--174.

[20]

Edward D. Lazowska, John Zahorjan, G. Scott Graham, and Kenneth C. Sevcik. 1984. Quantitative System Performance: Computer System Analysis Using Queueing Network Models. Prentice-Hall, Inc., Upper Saddle River, NJ, USA.

[21]

Jean-Marc Pierson and Helmut Hlavacs. 2015. Introduction to Energy Efficiency in Large-Scale Distributed Systems. In Large-Scale Distributed Systems and Energy Efficiency: A Holistic View. John Wiley & Sons, Inc, Hoboken, NJ, USA.

[22]

Altaf Ur Rahman, Fiaz Gul Khan, and Waqas Jadoon. 2016. Energy Efficiency techniques in cloud computing. International Journal of Computer Science and Information Security 14, 6 (2016), 317.

[23]

Swapnoneel Roy, Atri Rudra, and Akshat Verma. 2013. An energy complexity model for algorithms. In Proceedings of the 4th conference on Innovations in Theoretical Computer Science. ACM, 283--304.

Digital Library

[24]

Shuaiwen Leon Song, Kevin Barker, and Darren Kerbyson. 2013. Unified performance and power modeling of scientific workloads. In Proceedings of the 1st International Workshop on Energy Efficient Super computing. ACM, 4.

Digital Library

[25]

Yuan Tian, Chuang Lin, and Keqin Li. 2014. Managing performance and power consumption tradeoff for multiple heterogeneous servers in cloud computing. Cluster Computing 17, 3 (2014), 943--955.

Digital Library

[26]

Monica Vitali and Barbara Pernici. 2014. A survey on energy efficiency in information systems. International Journal of Cooperative Information Systems 23, 03 (2014).

[27]

Margaret H Wright. 2010. Nelder, Mead, and the other simplex method. Documenta Mathematica 7 (2010), 271--276.

[28]

Yuan Yao, Longbo Huang, Abhihshek Sharma, Leana Golubchik, and Michael Neely. 2012. Data centers power reduction: A two time scale approach for delay tolerant workloads. In INFOCOM, 2012 Proceedings IEEE. IEEE, 1431--1439.

Cited By

Huang NLi AZhang SZong Z(2023)Reducing Cloud Expenditures and Carbon Emissions via Virtual Machine Migration and Downsizing2023 IEEE International Performance, Computing, and Communications Conference (IPCCC)10.1109/IPCCC59175.2023.10253871(74-81)Online publication date: 17-Nov-2023
https://doi.org/10.1109/IPCCC59175.2023.10253871
Ho VHo NPedersen T(2023)Mining Seasonal Temporal Patterns in Time Series2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00174(2249-2261)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00174
Ho VHo NPedersen T(2022)Efficient temporal pattern mining in big time series using mutual informationProceedings of the VLDB Endowment10.14778/3494124.349414715:3(673-685)Online publication date: 4-Feb-2022
https://dl.acm.org/doi/10.14778/3494124.3494147
Show More Cited By

Improving Energy Efficiency for Transactional Workloads in Cloud Environments
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis

Recommendations

Improving Energy Efficiency in Data Centers and Federated Cloud Environments: Comparison of CoolEmAll and Eco2Clouds Approaches and Metrics
CGC '13: Proceedings of the 2013 International Conference on Cloud and Green Computing

Significant data centers energy footprints and the increase in energy prices have stimulated investigations into possible metrics and methods to define, quantify and improve the energy efficiency of data centers and federated cloud environments. Studies ...
Energy efficiency for MapReduce workloads: an in-depth study
ADC '12: Proceedings of the Twenty-Third Australasian Database Conference - Volume 124

Energy efficiency has emerged as a crucial optimization goal in data centers. MapReduce has become a popular and even fashionable distributed processing model for parallel computing in data centers. Hadoop is an open-source implementation of MapReduce, ...
Trade-off between Performance and Energy Management in Autonomic and Green Data Centers
NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & Security

In recent years, energy optimization of cloud data centers got important consideration since data centers in activity frequently expend huge energy rates. In fact, the expanding processing capacity of data centers and its complexity, increases ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems

May 2017

388 pages

ISBN:9781450350365

DOI:10.1145/3077839

Copyright © 2017 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 the author(s) 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

SIGCOMM: ACM Special Interest Group on Data Communication

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 May 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

e-Energy '17

Sponsor:

SIGCOMM

e-Energy '17: The Eighth International Conference on Future Energy Systems

May 16 - 19, 2017

Shatin, Hong Kong

Acceptance Rates

Overall Acceptance Rate 160 of 446 submissions, 36%

Upcoming Conference

E-Energy '25

Sponsor:
sigenergy

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

June 17 - 20, 2025

Rotterdam , Netherlands

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
133
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)1

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Huang NLi AZhang SZong Z(2023)Reducing Cloud Expenditures and Carbon Emissions via Virtual Machine Migration and Downsizing2023 IEEE International Performance, Computing, and Communications Conference (IPCCC)10.1109/IPCCC59175.2023.10253871(74-81)Online publication date: 17-Nov-2023
https://doi.org/10.1109/IPCCC59175.2023.10253871
Ho VHo NPedersen T(2023)Mining Seasonal Temporal Patterns in Time Series2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00174(2249-2261)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00174
Ho VHo NPedersen T(2022)Efficient temporal pattern mining in big time series using mutual informationProceedings of the VLDB Endowment10.14778/3494124.349414715:3(673-685)Online publication date: 4-Feb-2022
https://dl.acm.org/doi/10.14778/3494124.3494147
Ho NHo VBach Pedersen TVu M(2021)Efficient and Distributed Temporal Pattern Mining2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671753(335-343)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671753
Blanco BLiberal FLassila PAalto SSainz JGribaudo MPernici B(2018)Energy vs. QoX Network- and Cloud Services ManagementAutonomous Control for a Reliable Internet of Services10.1007/978-3-319-90415-3_10(241-268)Online publication date: 25-May-2018
https://doi.org/10.1007/978-3-319-90415-3_10

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.

Figures

Tables

Media

View Table of Conten