The goal of capacity planning is to ensure that you always have sufficient but not excessive resources to meet customers’ needs in a timely fashion. The game is acted out as a play in three acts.
The first act is to instrument (measure) what’s going on. As Mark Twain said in an interview with Rudyard Kipling, “Get your facts first, and then you can distort them as much as you please.”
The second act is to forecast the expected workloads (the demand to be placed on the system) and in the third act, you model various combinations to determine the least costly combination for getting the job done with the response times and service levels that you require. But as Shakespeare says in Hamlet’s soliloquy to Ophelia, “Aye, there’s the rub.”
Act Three of our play is a model . And this model has to be validated, which means proven correct. Mathematical induction cannot be extrapolated infinitely. If your measurements in Act One were based on 10 transactions a minute, and your forecast in Act Two is for 500 transactions a minute, then you model based on that forecast, and your model was not validated beyond 60 transactions a minute, it’s unlikely that the model will be accurate for predicting behavior at 500 transactions per minute. Capacity planning is iterative and requires that you constantly revalidate your models.
The key to success is making accurate assumptions.Classical assumptions for analysis include:
Models that have been verified over a range of real-life conditions are said to be robust and are useful for prediction.
Capacity Mangement: An Old-New Technique
Capacity management in the clouds is an old-new story. In the early 1970s and continuing through the 1980s, capacity management—configuring just the right configuration of resources to meet response time requirements at the lowest possible cost—was a hot field. Mainframe computers were complex to configure, had long lead times, and were expensive, often costing more than $1 million ($4 million, adjusted for inflation). It was vital to configure them correctly and to accurately model workloads and related resource utilizations to ensure acceptable response times. It is no less vital when deploying in the clouds. To configure correctly, we need a basic understanding of queuing theory.
Cloud Computing Related Interview Questions
|Adv Java Interview Questions||UNIX/XENIX Interview Questions|
|Red Hat Linux System Administration Interview Questions||Microsoft Azure Interview Questions|
|Amazon Web Services (AWS) Interview Questions||Unix/Linux Interview Questions|
|KVM Interview Questions||Linux Virtualization Interview Questions|
|Aws Cloud Architect Interview Questions||Salesforce Crm Interview Questions|
|Azure Cosmos DB Interview Questions|
Cloud Computing Related Practice Tests
|Adv Java Practice Tests||UNIX/XENIX Practice Tests|
|Red Hat Linux System Administration Practice Tests||Microsoft Azure Practice Tests|
|Amazon Web Services (AWS) Practice Tests|
Cloud Computing Tutorial
Cloud Computing Is A True Paradigm Shift
From Do It Yourself To Public Cloud—a Continuum
Cloud Computing: Is It Old Mainframe Bess In A New Dress?
Moving Into And Around The Clouds And Efforts At Standardization
Cloud Economics And Capacity Management
Demystifying The Cloud: A Case Study Using Amazon’s Cloud Services (aws)
Virtualization: Open Source And Vmware
Securing The Cloud: Reliability, Availability, And Security
Scale And Reuse: Standing On The Shoulders Of Giants
Google In The Cloud
Enterprise Cloud Vendors
Cloud Service Providers
Practice Fusion Case Study
All rights reserved © 2018 Wisdom IT Services India Pvt. Ltd
Wisdomjobs.com is one of the best job search sites in India.