[Autoscaler][Discuss] Stratos Autoscaler now supports predictive appreaoch beyond reactive Horizontal Autoscaling

[Lahiru Sandaruwan <la...@wso2.com>]

Hi all,

$subject is done.
You can get an idea about Autoscaling technologies following my hangout on
Autoscaler(Slides [1] and record [2]). [3] also explains this to a great
extend.

*What is done now?*

   - Autoscaler receive summarized values such as average, gradient, and
   second derivative of stats like, LB requests in flight, Memory consumption
   and Load average from CEP
   - Then it will predict the values of the stats for a time duration(can
   be given from Drools file)
   - Use famous S = u*t + .5*a*t*t motion equation to predict. This is one
   of basics of *Kalman filter*. Here the statistics value is mapped to the
   displacement of an object which is in linear motion with constant
   acceleration.
   - Compare predicted values with given threshold to get decisions on
   scale up and down

So in the current approach, as the first step, it is still using a
threshold value to get the decision. There are lot of improvements that can
be done.


*To be done in future*


   - Decide number of instances to be scale up/ down according to the
   predicted value(Amazon EC2 has a good model)
   - Use better prediction approaches such as control theory to predict
   - Use approaches like time series analysis to detect pattern and be
   proactive on load
   - Make Autoscaler works on pre-defined timely patterns (E.g. Yearly
   pattern to handle New year hikes)
   - Consider application dependencies while scaling.

Please send your thoughts over.

Thanks.

[1]
http://www.slideshare.net/hettiarachchigls1/autoscaler-architecture-of-apache-stratos-400
[2] http://www.youtube.com/watch?v=DyWtCXT8Vqk
[3]
http://www.sc.ehu.es/ccwbayes/isg/administrator/components/com_jresearch/files/publications/autoscaling.pdf

-- 
--
Lahiru Sandaruwan
Software Engineer,
Platform Technologies,
WSO2 Inc., http://wso2.com
lean.enterprise.middleware

email: lahirus@wso2.com cell: (+94) 773 325 954
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