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Utilizing Green Energy Prediction to Adapt to Energy Supply Variability when Scheduling Mixed
Batch and Service Jobs in Data Centers
Renewable energy prediction
• Instantaneous renewable energy usage may result in power shortages • Use short term prediction algorithms (30min window) to decide when renewable energy to run additional
processing requests in order to achieve higher energy efficiency while meeting performance constraints
Motivation • Data centers consume a lot of power
• Millions of MWh, reflected as billions of dollars in the electricity bills • Tons of carbon emissions to the atmosphere
• Idea: Use renewable energy to reduce carbon emissions • Problem: Renewable energy supply is highly variable which decreases
the energy usage efficiency. Data center power consumption [Hitachi Eco-friendly
Data center project, http://www.hitachi.com]
Baris Aksanli, Jagannathan Venkatesh, Liuyi Zhang and Tajana Rosing UC San Diego
NSF Expedi t ion in Comput ing, Var iab i l i ty -Aware Sof tware for Eff ic ient Comput ing wi th Nanoscale Devices h t tp : / /var iab i l i ty.org
• EWMA -> 32.6 % error • Extended EWMA -> 23.4% error •Our work: WCMA -> 9.6% error
•Combination of a weighted nearest-neighbor (NN) tables and wind power curve models •21.2% error vs. state-of-the-
art predictor 48.2% error
System Architecture
• Use green energy to schedule more batch jobs – MapReduce jobs are good as 92% finish within 30min
• Terminate MR task when green energy supply drops • Guarantee service jobs meet their performance
constraints, and limit performance hit to batch job completion times Results
•Prediction has 15% lower job completion time on average compared to instantaneous case
•Prediction has 2x overall higher GE efficiency vs. instantaneous • GE Efficiency: % of the GE
that is used for useful work
•Prediction leads to ~2x more jobs completed with green energy vs. instantaneous case • GE Job Percentage: ratio of
jobs completed with GE over
all completed jobs
•On average, 5x fewer batch tasks need to be terminated with prediction. • %Incomplete Jobs: ratio
of terminated jobs over all
jobs completed with GE
Comparison of solar prediction algorithms. Data gathered from solar installation at UCSD • Comparison of wind prediction algorithms.
Data gathered from a wind farm in Lake Benton, available by NREL
Baris Aksanli, Jagannathan Venkatesh, Liuyi Zhang and Tajana Rosing. Utilizing Green Energy Prediction to Schedule Mixed Batch and Service Jobs in Data Centers. International Workshop on Power Aware Computing and Systems, HotPower, 2011.
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