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Lahiru Sandaruwan commented on STRATOS1211:

Hi,
What is the subject of the thread?
Thanks.
> Introducing "curve fitting" for stat prediction algorithm of Autoscaler
> 
>
> Key: STRATOS1211
> URL: https://issues.apache.org/jira/browse/STRATOS1211
> Project: Stratos
> Issue Type: Bug
> Components: Autoscaler, CEP
> Reporter: Lahiru Sandaruwan
> Labels: gsoc2015
> Fix For: FUTURE
>
>
> This is a summery of a mail sent to Stratos dev under "[Autoscaling] [Improvement] Introducing
"curve fitting" for stat prediction algorithm of Autoscaler" subject.
> Current implementation
> Currently CEP calculates average, gradient, and second derivative and send those values
to Autoscaler. Then Autoscaler predicts the values using S = u*t + 0.5*a*t*t.
> In this method CEP calculation is not very much accurate as it does not consider all
the events when calculating the gradient and second derivative. Therefore the equation we
apply doesn't yield the best prediction.
> Proposed Implementation
> CEP's task
> I think best approach is to do "curve fitting"[1] for received event sample in a particular
time window. Refer "Locally weighted linear regression" section at [2] for more details.
> We would need a second degree polynomial fitter for this, where we can use Apache commons
math library for this. Refer the sample at [3], we can run this with any degree. e.g. 2, 3.
Just increase the degree to increase the accuracy.
> E.g.
> So if get degree 2 polynomial fitter, we will have an equation like below where value(v)
is our statistic value and time(t) is the time of event.
> Equation we get from received events,
> v = a*t*t + b*t + c
> So the solution is,
> Find memberwise curves that fits events received in specific window(say 10 minutes) at
CEP
> Send the parameters of fitted line(a, b, and c in above equation) with the timestamp
of last event(T) in the window, to Autoscaler
> Autoscaler's task
> Autoscaler use v = a*t*t + b*t + c function to predict the value in any timestamp from
the last timestamp
> E.g. Say we need to find the value(v) after 1 minute(assuming we carried all the calculations
in milliseconds),
> v = a * (T+60000) * (T+60000) + b * (T+60000) + c
> So we have memberwise predictions and we can find clusterwise prediction by averaging
all the memberwise values.

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