[infinispan-dev] Auto Scaling for Infinispan

[Paolo Romano]

Currently it is more geared towards auto-provisioning... but we're 
working to develop forecasting models also for other 
replication/distribution mechanisms, and to use them to understand how 
to respond to workload fluctuations over multiple dimensions: not only 
how many nodes to use to maximize performance/minimize costs, but also 
what data management algorithms to use.

Cheers,

     P

On 12/9/11 4:12 PM, Manik Surtani wrote:
> So from a CloudTM perspective, this is a part of the adaptive, self-tuning configuration aspect of things?  Or is it auto-provisioning (i.e., adding/removing nodes to meet QoS parameters)?
>
> - Manik
>
> On 4 Dec 2011, at 13:37, Paolo Romano wrote:
>
>> Hi,
>>
>> one more result from the Cloud-TM project that we thought might be
>> interesting for the Infinispan community (and possibly OpenShift).
>>
>> Our last effort is a system for automating elastic scaling for
>> Infinispan, which we named TAS: Transactional Auto Scaler.
>>
>> TAS uses a hybrid methodology combining analytical modelling, for
>> forecasting the effects of data contention, and machine learning
>> techniques (we experimented both with Radial Basis Functions Aritificial
>> Neural Networks and Regression Decision Trees), for forecasting the
>> effects of contention on hardware resources.
>>
>> Applications of TAS range from on-line self-optimization of
>> in-production applications, to the automatic generation of QoS/cost
>> driven elastic scaling policies, and support for what-if analysis on the
>> scalability of transactional applications.
>>
>> Results look pretty good, probably even better than we were originally
>> hoping! ;-)
>>
>> Cheers,
>>
>>      Paolo
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> --
> Manik Surtani
> manik at jboss.org
> twitter.com/maniksurtani
>
> Lead, Infinispan
> http://www.infinispan.org
>
>
>
>
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