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https://issues.apache.org/jira/browse/STRATOS-1211?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14350309#comment-14350309
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Lahiru Sandaruwan commented on STRATOS-1211:
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Hi,
It is good to hear from you. It would be better to communicated through
dev(dev@stratos.apache.org ) mailing list.
First, get some idea in Stratos. You can introduce yourself and request for the
information you need at dev@.
Find the latest guides at [1].
You can find the source for Apache Stratos on Github[2].
Thanks.
[1] https://cwiki.apache.org/confluence/display/STRATOS/4.1.0
[2] - https://github.com/apache/stratos
> Introducing "curve fitting" for stat prediction algorithm of Autoscaler
> -----------------------------------------------------------------------
>
> Key: STRATOS-1211
> URL: https://issues.apache.org/jira/browse/STRATOS-1211
> 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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