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. 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. 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
12. 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
