Status quo
Status quo
Status quo
Status quo
Energy, Let’s save it !http://www.youtube.com/watch?v=1-g73ty9v04
Data Center Power usage stats• Prediction : The influential report issued by the E.P.A. in August of 2007  estimated that ...
Power saving, Machine Learning based   scheduler for HPC Data Centers• Algorithm    – ML aspects of it    – Complexity    ...
Algorithm Poll hosts for information about their jobs and status;    OH := select "Emptiable Machines" [jobs < 4];   For...
Programhttps://github.com/codeathon/SchedulerHPC
Hurdles• Power Usage calculation & prediction  – Linear regression relation with CPU Usage based on    relevant attributes...
Parallel & Distributed Computing
Parallel & Distributed Computing
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Parallel & Distributed Computing

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Parallel & Distributed Computing

  1. 1. Status quo
  2. 2. Status quo
  3. 3. Status quo
  4. 4. Status quo
  5. 5. Energy, Let’s save it !http://www.youtube.com/watch?v=1-g73ty9v04
  6. 6. Data Center Power usage stats• Prediction : The influential report issued by the E.P.A. in August of 2007 estimated that national energy consumption by computer servers and data centers would nearly double from 2005 to 2010 to roughly 100 billion kilowatt hours of energy at an annual cost of $7.4 billion. It predicted the centers’ demand for power in the United States would rise by 2011 to 12 gigawatts of power, or the output of 25 major power plants, from 7 gigawatts, or about 15 power plants.• The financial implications are significant; estimates of annual power costs for U.S. data centers now range as high as $3.3 billion. This trend impacts data center capacity as well. According to the Fall 2007 Survey of the Data Center Users Group (DCUG®), an influential group of data center managers, power limitations were cited as the primary factor limiting growth by 46 percent of respondents, more than any other factor. In addition to financial and capacity considerations, reducing data center energy use has become a priority for organizations seeking to reduce their environmental footprint.
  7. 7. Power saving, Machine Learning based scheduler for HPC Data Centers• Algorithm – ML aspects of it – Complexity – Implementation (Simulation + Real) • Performance evaluation & prediction• for the upcoming week)
  8. 8. Algorithm Poll hosts for information about their jobs and status;  OH := select "Emptiable Machines" [jobs < 4]; For each Machine (h) in Cluster do: For each Job (j) in Machine(h) do:  CH := select "Fillable Machines" [enough CPU and mem]; For each Machine (ch) in CH do: -- predict effect of moving j from oh to ch;  predict R(h) and R(ch) after movement; predict C(h) and C(ch) after movement; compute global R and C after movement; End For Get ch leading to highest R among those that decrease C; add movement (j,h,ch) to List_of_movements; End For If (all jobs in h can be reallocated) then: proceed with the List_of_movements; End If End For
  9. 9. Programhttps://github.com/codeathon/SchedulerHPC
  10. 10. Hurdles• Power Usage calculation & prediction – Linear regression relation with CPU Usage based on relevant attributes like cpu_time, walltime, mem_used, vmem_used, num_of _jobs• Task Migration / Job Moving – Combine the Performance calculation & CPU Usage calculation to identify a good task candidate for migration• A Simulation environment

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