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Reading: ReBlog: On Why I Don’t Like Auto-Scaling in the Cloud
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SmartData Collective > Big Data > Data Mining > ReBlog: On Why I Don’t Like Auto-Scaling in the Cloud
Business IntelligenceData MiningData WarehousingPredictive Analytics

ReBlog: On Why I Don’t Like Auto-Scaling in the Cloud

TonyBain
TonyBain
2 Min Read
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George Reese posted the following on the O’Reilly Blog “I enter into sales meetings getting clients excited about dynamic scaling only having to vigorously talk them away from the idea of auto-scaling. I just don’t like auto-scaling.” My reply to…

George Reese posted the following on the O’Reilly Blog

“I enter into sales meetings getting clients excited about dynamic scaling only having to vigorously talk them away from the idea of auto-scaling. I just don’t like auto-scaling.”

My reply to this was as follows (I am re-posting this here as it is relevant to the chain of posts I am working on at the moment).

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George, you are dismissing a logical concept, “auto sizing”, based on current physical implementations of that concept.  A model where we don’t have to worry about manually planning application resources requirements because they are automatically allocated based on needs makes sense and certainly seems to the right direction to be heading.  One can predict a likely point in the not too distant future where planning I/O and other resource demands in detail will cease to be relevant and service providers charging metrics, such as based on network traffic, will become outdated just as charging based on number of CPU instructions or CPU time ceased to be (and charging based on number of database transactions used ceased to be, and charging based on disk space is starting to be).  Capacity planning as a discipline is becoming more holistic as the detail becomes less relevant to us humans, the focus will be on planning the capacity of an “environment” and leaving the individual allocations of applications that run in that environment up to it.

So I will agree with you that Auto Sizing systems are not necessarily fool proof or should be trusted, but only for now.

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