Íñigo Goiri
Ryan Beauchea
Jordi Guitart
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.
- 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.Google Scholar
Cross Ref
- L. A. Barroso and U. Hölzle. The Case for Energy-Proportional Computing. IEEE Computer, 40(12), December 2007. Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarCross Ref
- DSIRE. Database of State Incentives for Renewables and Efficiency. http://www.dsireusa.org/.Google Scholar
- 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.Google Scholar
- O. Flores and M. Orozco. NucleR: A Package for Non-Parametric Nucleosome Positioning. Bioinformatics, 2011. Google ScholarDigital Library
- Global Action Plan. An Inefficient Truth, December 2007. http://globalactionplanorguk.site.securepod.com/upload/resource/-Exec-Summary.pdf.Google Scholar
- M. Islam. QoS in Parallel Job Scheduling. PhD thesis, Dept. of Computer Science and Engineering, Ohio State University, 2008. Google ScholarDigital Library
- S. Jebaraj and S. Iniyan. A Review of Energy Models. Renewable and Sustainable Energy Reviews, 10(4), August 2006.Google Scholar
- A. Jossen, J. Garche, and D. Sauer. Operation conditions of batteries in pv applications. Solar Energy, 76(6):759--769, 2004.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- K. Le, R. Bianchini, M. Martonosi, and T. D. Nguyen. Cost- And Energy-Aware Load Distribution Across Data Centers. In Proceedings of HotPower, 2009.Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google Scholar
- 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. Google ScholarDigital Library
- D. Lifka. The ANL/IBM SP Scheduling System. In Proceedings of the International Workshop on Job Scheduling Strategies for Parallel Processing, 1995. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Power Scorecard. Electricity from Coal. http://www.powerscorecard.org/tech_detail.cfm?resource_id=2.Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- SolarBuzz. Marketbuzz, 2011. http://www.solarbuzz.com/our-research/reports/marketbuzz.Google Scholar
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- UK Government. Carbon Reduction Commitment. http://www.carbonreductioncommitment.info/.Google Scholar
- US Environmental Protection Agency. Report to Congress on Server and Data Center Energy Efficiency, August 2007.Google Scholar
Index Terms
- GreenSlot: scheduling energy consumption in green datacenters
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