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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:
--------------------------------------------

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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