ABSTRACT
As brown energy costs grow, renewable energy becomes more widely used. Previous work focused on using immediately available green energy to supplement the non-renewable, or brown energy at the cost of canceling and rescheduling jobs whenever the green energy availability is too low [16]. In this paper we design an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production. This enables us to scale the number of jobs to the expected energy availability, thus reducing the number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy.
- D. Gmach, J. Rolia, C. Bash, Y. Chen, T. Christian, A. Shah, R. Sharma and Z. Wang. "Capacity Planning and Power Management to Exploit Sustainable Energy". International Conference on Network and Service Management. CNSM'10. 2010.Google ScholarCross Ref
- D. Gmach, Y. Chen, A. Shah, J. Rolia, C. Bash, T. Christian, R. Sharma. "Profiling sustainability of data centers". Sustainable Systems and Technology (ISSST), 2010 IEEE International Symposium. 2010Google Scholar
- K. Le, R. Bianchini, T. D. Nguyen, O. Bilgir, M. Martonosi. "Capping the brown energy consumption of Internet services at low cost". In Proceedings of the International Conference on Green Computing. 2010. Google ScholarDigital Library
- C. Stewart and K. Shen. 'Some Joules Are More Precious Than Others: Managing Renewable Energy in the Datacenter". 4th Workshop on Power-Aware Computing and Systems. HotPower'09. 2009.Google Scholar
- G. Malewicz, M. H. Austern, A. J. C Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. 2010. Pregel: a system for large-scale graph processing. In Proceedings of the 2010 international conference on Management of data (SIGMOD '10). Google ScholarDigital Library
- Rubis. http://rubis.ow2.org/Google Scholar
- C. Tang, S. Tara, R. Chang, C. Zhang. "Black-Box Performance Control For High-Volume Non-Interactive Systems". USENIX '09. 2009. Google ScholarDigital Library
- Hadoop. http://hadoop.apache.org/Google Scholar
- S. Kavulya, J. Tan, R. Gandhi and P. Narasimhan. "An Analysis of Traces from a Production MapReduce Cluster". Carnegie Mellon University, Parallel Data Lab. Techinal Report. DOI: CMU-PDL-09-107.Google Scholar
- D. Economou, S. Rivoire, C. Kozyrakis, P. Ranganathan. "Full system power analysis and modeling for server environments". In Workshop on Modeling Benchmarking and Simulation (MOBS), June 2006.Google Scholar
- D. Ersoz, M. S. Yousif, and C. R. Das. "Characterizing Network Traffic in a Cluster-based, Multi-tier Data Center". In Proceedings of the 27th International Conference on Distributed Computing Systems (ICDCS). Google ScholarDigital Library
- Intel Microarchitecture Nehalem. http://www.intel.com/technology/architecture-silicon/next-genGoogle Scholar
- Luiz André Barroso, Urs Hölzle. The data center as a computer: An Introduction to the Design of Warehouse-Scale Machines, 2009.Google Scholar
- National Renewable Energy Laboratory. http://www.nrel.gov/Google Scholar
- Energy Recommerce. http://www.mypvdata.com/Google Scholar
- A. Krioukov, C. Goebel, S. Asplaugh, Y. Chen, D. Culler, R. Katz. "Integrating Renewable Energy Using Data Analytics Systems: Challenges and Opportunities". IEEE Data Engineering Bulletin. March 2011.Google Scholar
- J. Tanega. Towards Cooperative Grids: Sensor/Actuator Networks for Renewables Integration. Sensys 2010 Doctoral Colloquium. 2010.Google Scholar
- B. Watson, A. Shah, M. Marwah, C. Bash, R. Sharma, C. Hoover, T. Christian, C. Patel. Integrated Design and Management of a Sustainable Data Center. Proceedings of IPACK2009. 2009.Google ScholarCross Ref
- C. Holt. Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 2004.Google ScholarCross Ref
- J. Piorno, C. Bergonzini, D. Atienza, T. Rosing. Prediction and management in energy harvested wireless sensor nodes. University of California, San Diego, 2010.Google Scholar
- A. Kusaik, H. Zheng, Z. Song. "Short term prediction of wind farm power: A Data Mining approach". IEEE TEC, Vol. 24, No. 1, pp. 125--136, March 2009.Google ScholarCross Ref
- G. Giebel, R Brownsword, G Kariniotakis. "The State-of-the-Art in short-term prediction of wind power -- A literature overview". Project ANEMOS Deliverable Report. August 2003.Google Scholar
- D. Meisner, T. F. Wenisch. "Stochastic queuing simulation for data center workloads". Proc. of the Workshop on Exascale Evaluation and Research Techniques (EXERT), Mar. 2010.Google Scholar
- I. Sanchez: "Short-term prediction of wind energy production". International Journal of Forecasting, Vol. 22, pp 43--56. 2006.Google ScholarCross Ref
- J. Dean and S. Ghemawat. 2008. MapReduce: simplified data processing on large clusters. Commun. ACM 51, 1 (January 2008), 107--113. Google ScholarDigital Library
- M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. 2007. Dryad: distributed data-parallel programs from sequential building blocks. SIGOPS Oper. Syst. Rev. 41, 3 (March 2007), 59--72. Google ScholarDigital Library
Index Terms
- Utilizing green energy prediction to schedule mixed batch and service jobs in data centers
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