We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
    7 Min Read
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
    chatgpt image jul 13, 2026, 03 59 46 pm
    How Data Analytics Improves Multi-Location Search Strategies
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: ReBlog: On Why I Don’t Like Auto-Scaling in the Cloud
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
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
ReBlog: On Why I Don't Like Auto-Scaling in the Cloud
Illustration generated with Qwen Image.
SHARE

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).

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.

More Read

Social processes: alternative mode of discourse
Social processes: alternative mode of discourse
Dresner: Mobile Business Intelligence to Transform BI Industry
Why Projects Fail: The Biggest Pitfalls You Can Easily Avoid
Analyst, Scientist, or Specialist? Choosing Your Data Job Title
Metadata versus Taxonomy

Link to original postInnovations in information management

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Technical architecture diagram and decision framework for ai-powered logo generation: design.com vs looka
AI-Powered Logo Generation: Design.com vs Looka
Artificial Intelligence Exclusive
Retailers Should Stop Treating Every Stockout as Equal -- AI-generated illustration
Retailers Should Stop Treating Every Stockout as Equal
Business Intelligence Exclusive
Best Vibe Coding Cleanup Specialists in the USA: Fix or Rebuild? -- AI-generated illustration
Best Vibe Coding Cleanup Specialists in the USA: Fix or Rebuild?
Development Exclusive
Using Warehouse, Transportation and Order Data to Plan Distribution-Center Capacity -- AI-generated illustration
Using Warehouse, Transportation and Order Data to Plan Distribution-Center Capacity
Big Data Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

4 Best Practices for Sharing Workforce Data: Publishing Core Reports
AnalyticsData MiningKnowledge Management

4 Best Practices for Sharing Workforce Data: Publishing Core Reports

5 Min Read
SAS adds support to R
Data Mining

SAS adds support to R

6 Min Read
How Are Predictive Analytics Shaping the Future of Fintech?
AnalyticsFintechPredictive Analytics

How Are Predictive Analytics Shaping the Future of Fintech?

6 Min Read
Why it should be “target & test”, not “test & target”
Data Mining

Why it should be “target & test”, not “test & target”

6 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

How To Get An Award Winning Giveaway Bot
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?