research-article

GreenSlot: scheduling energy consumption in green datacenters

Authors:

Ricardo BianchiniAuthors Info & Claims

SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 20, Pages 1 - 11

https://doi.org/10.1145/2063384.2063411

Published: 12 November 2011 Publication History

Abstract

In this paper, we propose GreenSlot, a parallel batch job scheduler for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenSlot predicts the amount of solar energy that will be available in the near future, and schedules the workload to maximize the green energy consumption while meeting the jobs' deadlines. If grid energy must be used to avoid deadline violations, the scheduler selects times when it is cheap. Our results for production scientific workloads demonstrate that Green-Slot can increase green energy consumption by up to 117% and decrease energy cost by up to 39%, compared to a conventional scheduler. Based on these positive results, we conclude that green datacenters and green-energy-aware scheduling can have a significant role in building a more sustainable IT ecosystem.

References

[1]

A. Yoo and M. Jette and M. Grondona. SLURM: Simple Linux Utility for Resource Management. In Proceedings of the International Workshop on Job Scheduling Strategies for Parallel Processing, June 2003.

[2]

L. A. Barroso and U. Hölzle. The Case for Energy-Proportional Computing. IEEE Computer, 40(12), December 2007.

Digital Library

[3]

R. Davis and A. Burns. A Survey of Hard Real-Time Scheduling Algorithms and Schedulability Analysis Techniques for Multiprocessor Systems. Technical Report YCS-2009-443, Dept. of Computer Science, University of York, 2009.

[4]

E. Deelman, J. Blythe, Y. Gil, C. Kesselman, G. Mehta, K. Vahi, K. Blackburn, A. Lazzarini, A. Arbree, R. Cavanaugh, and S. Koranda. Mapping Abstract Complex Workflows Onto Grid Environments. Journal of Grid Computing, 1(1):25--39, 2003.

[5]

DSIRE. Database of State Incentives for Renewables and Efficiency. http://www.dsireusa.org/.

[6]

D. Feitelson, L. Rudolph, and U. Schwiegelshohn. Parallel Job Scheduling -- A Status Report. In Proceedings of the International Workshop on Job Scheduling Strategies for Parallel Processing, 2004.

[7]

O. Flores and M. Orozco. NucleR: A Package for Non-Parametric Nucleosome Positioning. Bioinformatics, 2011.

Digital Library

[8]

Global Action Plan. An Inefficient Truth, December 2007. http://globalactionplanorguk.site.securepod.com/upload/resource/-Exec-Summary.pdf.

[9]

M. Islam. QoS in Parallel Job Scheduling. PhD thesis, Dept. of Computer Science and Engineering, Ohio State University, 2008.

Digital Library

[10]

S. Jebaraj and S. Iniyan. A Review of Energy Models. Renewable and Sustainable Energy Reviews, 10(4), August 2006.

[11]

A. Jossen, J. Garche, and D. Sauer. Operation conditions of batteries in pv applications. Solar Energy, 76(6):759--769, 2004.

[12]

K. Kant, M. Murugan, and D. H. C. Du. Willow: A Control System for Energy and Thermal Adaptive Computing. In Proceedings of the International Parallel and Distributed Processing Symposium, May 2011.

Digital Library

[13]

K. Le, R. Bianchini, M. Martonosi, and T. D. Nguyen. Cost- And Energy-Aware Load Distribution Across Data Centers. In Proceedings of HotPower, 2009.

[14]

K. Le, O. Bilgir, R. Bianchini, M. Martonosi, and T. D. Nguyen. Capping the Brown Energy Consumption of Internet Services at Low Cost. In Proceedings of the International Green Computing Conference, August 2010.

Digital Library

[15]

K. Le, J. Zhang, J. Meng, Y. Jaluria, T. D. Nguyen, and R. Bianchini. Reducing Electricity Cost Through Virtual Machine Placement in High Performance Computing Clouds. In Proceedings of Supercomputing, November 2011.

Digital Library

[16]

C. Lee, Y. Schwartzman, J. Hardy, and A. Snavely. Are User Runtime Estimates Inherently Inaccurate? In Proceedings of the International Workshop on Job Scheduling Strategies for Parallel Processing, 2004.

[17]

C. Li, W. Zhang, C. Cho, and T. Li. SolarCore: Solar Energy Driven Multi-core Architecture Power Management. In Proceedings of the International Symposium on High-Performance Computer Architecture, February 2011.

Digital Library

[18]

D. Lifka. The ANL/IBM SP Scheduling System. In Proceedings of the International Workshop on Job Scheduling Strategies for Parallel Processing, 1995.

Digital Library

[19]

Z. Liu, M. Lin, A. Wierman, S. Low, and L. Andrew. Greening Geographical Load Balancing. In Proceedings of the International Conference on Measurement and Modeling of Computer Systems, June 2011.

Digital Library

[20]

A. W. Mu'alem and D. G. Feitelson. Utilization, Predictability, Workloads, and User Runtime Estimates in Scheduling the IBM SP2 with Backfilling. IEEE Transactions on Parallel and Distributed Systems, 12(6):529--543, 2001.

Digital Library

[21]

Power Scorecard. Electricity from Coal. http://www.powerscorecard.org/tech_detail.cfm?resource_id=2.

[22]

A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs. Cutting the Electric Bill for Internet-Scale Systems. In Proceedings of SIGCOMM, August 2009.

Digital Library

[23]

P. Ranganathan, P. Leech, D. Irwin, and J. Chase. Ensemble-level Power Management for Dense Blade Servers. In Proceedings of the International Symposium on Computer Architecture, June 2006.

Digital Library

[24]

I. Rodero, F. Guim, and J. Corbalan. Evaluation of Coordinated Grid Scheduling Strategies. In Proceedings of the International Conference on High-Performance Computing and Communications, 2009.

Digital Library

[25]

N. Sharma, S. Barker, D. Irwin, and P. Shenoy. Blink: Managing Server Clusters on Intermittent Power. In Proceeding of the 16th International Conference on Architectural Support for Programming Languages and Operating Systems, March 2011.

Digital Library

[26]

N. Sharma, J. Gummeson, D. Irwin, and P. Shenoy. Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems. In Proceeding of the International Conference on Sensor Mesh and Ad Hoc Communications and Networks, June 2010.

[27]

J. Sherwani, N. Ali, N. Lotia, Z. Hayat, and R. Buyya. Libra: A Computational Economy-Based Job Scheduling System for Clusters. Software Practice and Experience, 34(6), May 2004.

Digital Library

[28]

SolarBuzz. Marketbuzz, 2011. http://www.solarbuzz.com/our-research/reports/marketbuzz.

[29]

C. Stewart and K. Shen. Some Joules Are More Precious Than Others: Managing Renewable Energy in the Datacenter. In Proceedings of the Workshop on Power Aware Computing and Systems, October 2009.

[30]

D. Talby and D. Feitelson. Supporting Priorities and Improving Utilization of the IBM SP2 Scheduler Using Slack-Based Backfilling. In Proceedings of the International Parallel Processing Symposium, April 1997.

Digital Library

[31]

D. Tsafrir, Y. Etsion, and D. G. Feitelson. Backfilling Using System-Generated Predictions Rather Than User Runtime Estimates. IEEE Transactions on Parallel and Distributed Systems, 18(6):789--803, 2007.

Digital Library

[32]

UK Government. Carbon Reduction Commitment. http://www.carbonreductioncommitment.info/.

[33]

US Environmental Protection Agency. Report to Congress on Server and Data Center Energy Efficiency, August 2007.

Cited By

Liu LMiao ZNan HLi WPan XYang XYu MChen HZhao Y(2024)Energy and Carbon-aware Distributed Machine Learning Tasks Scheduling Scheme for the Multi-Renewable Energy-based Edge-Cloud ContinuumScience and Technology for Energy Transition10.2516/stet/2024076Online publication date: 2-Sep-2024
https://doi.org/10.2516/stet/2024076
Kozlov OStamatakis A(2024)EcoFreq: Compute with Cheaper, Cleaner Energy via Carbon-Aware Power ScalingISC High Performance 2024 Research Paper Proceedings (39th International Conference)10.23919/ISC.2024.10528928(1-12)Online publication date: May-2024
https://doi.org/10.23919/ISC.2024.10528928
Cao ZZhou XWu XZhu ZLiu TNeng JWen Y(2024)Data Center Sustainability: Revisits and OutlooksIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.32815839:3(236-248)Online publication date: May-2024
https://doi.org/10.1109/TSUSC.2023.3281583
Show More Cited By

Index Terms

GreenSlot: scheduling energy consumption in green datacenters
1. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Process management

Recommendations

GreenHadoop: leveraging green energy in data-processing frameworks
EuroSys '12: Proceedings of the 7th ACM european conference on Computer Systems

Interest has been growing in powering datacenters (at least partially) with renewable or "green" sources of energy, such as solar or wind. However, it is challenging to use these sources because, unlike the "brown" (carbon-intensive) energy drawn from ...
Parasol and GreenSwitch: managing datacenters powered by renewable energy
ASPLOS '13

Several companies have recently announced plans to build "green" datacenters, i.e. datacenters partially or completely powered by renewable energy. These datacenters will either generate their own renewable energy or draw it directly from an existing ...
Matching renewable energy supply and demand in green datacenters

In this paper, we propose GreenSlot, a scheduler for parallel batch jobs in a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenSlot predicts the amount of solar energy that will be available in the near future,...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis

November 2011

866 pages

ISBN:9781450307710

DOI:10.1145/2063384

Conference Chair:
Scott Lathrop
University of Chicago
,
Program Chairs:
Jim Costa
Sandia National Laboratories
,
William Kramer
National Center for Supercomputing Applications

Copyright © 2011 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

SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 November 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

SC '11

Sponsor:

SIGARCH
IEEE-CS

SC '11: International Conference for High Performance Computing, Networking, Storage and Analysis

November 12 - 18, 2011

Washington, Seattle

Acceptance Rates

SC '11 Paper Acceptance Rate 74 of 352 submissions, 21%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

196
Total Citations
View Citations
1,199
Total Downloads

Downloads (Last 12 months)92
Downloads (Last 6 weeks)6

Reflects downloads up to 20 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liu LMiao ZNan HLi WPan XYang XYu MChen HZhao Y(2024)Energy and Carbon-aware Distributed Machine Learning Tasks Scheduling Scheme for the Multi-Renewable Energy-based Edge-Cloud ContinuumScience and Technology for Energy Transition10.2516/stet/2024076Online publication date: 2-Sep-2024
https://doi.org/10.2516/stet/2024076
Kozlov OStamatakis A(2024)EcoFreq: Compute with Cheaper, Cleaner Energy via Carbon-Aware Power ScalingISC High Performance 2024 Research Paper Proceedings (39th International Conference)10.23919/ISC.2024.10528928(1-12)Online publication date: May-2024
https://doi.org/10.23919/ISC.2024.10528928
Cao ZZhou XWu XZhu ZLiu TNeng JWen Y(2024)Data Center Sustainability: Revisits and OutlooksIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.32815839:3(236-248)Online publication date: May-2024
https://doi.org/10.1109/TSUSC.2023.3281583
Xing JLee B(2024)Datacenter Demand Response for Carbon Mitigation: From Concept to Practicality : Invited Paper2024 IEEE 15th International Green and Sustainable Computing Conference (IGSC)10.1109/IGSC64514.2024.00034(142-144)Online publication date: 2-Nov-2024
https://doi.org/10.1109/IGSC64514.2024.00034
Chadha MSubramanian TArima EGerndt MSchulz MAbboud O(2023)GreenCourierProceedings of the 9th International Workshop on Serverless Computing10.1145/3631295.3631396(18-23)Online publication date: 11-Dec-2023
https://dl.acm.org/doi/10.1145/3631295.3631396
Bashir NChandio YIrwin DAnwar FGummeson JShenoy P(2023)Jointly Managing Electrical and Thermal Energy in Solar- and Battery-powered Computer SystemsProceedings of the 14th ACM International Conference on Future Energy Systems10.1145/3575813.3595191(132-143)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3575813.3595191
Chakraborty TToosi AKopp C(2023) Elastic Power Utilization in Sustainable Micro Cloud Data Centers IEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.32365988:3(465-478)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TSUSC.2023.3236598
Radovanović AKoningstein RSchneider IChen BDuarte ARoy BXiao DHaridasan MHung PCare NTalukdar SMullen ESmith KCottman MCirne W(2023)Carbon-Aware Computing for DatacentersIEEE Transactions on Power Systems10.1109/TPWRS.2022.317325038:2(1270-1280)Online publication date: Mar-2023
https://doi.org/10.1109/TPWRS.2022.3173250
Liu C(2023)Research on quantitative evaluation method of demand response of new load park based on real-time data2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA)10.1109/ICPECA56706.2023.10075847(1542-1548)Online publication date: 29-Jan-2023
https://doi.org/10.1109/ICPECA56706.2023.10075847
Bahreini TTantawi AYoussef A(2023)A Carbon-aware Workload Dispatcher in Cloud Computing Systems2023 IEEE 16th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD60044.2023.00032(212-218)Online publication date: Jul-2023
https://doi.org/10.1109/CLOUD60044.2023.00032
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.

Figures

Tables

Media

View Table of Conten