Automating Elasticity
AWS Whitepaper

Automating Elasticity

Publication date: March 2018 (Document Details)

Abstract

This is the sixth in a series of whitepapers designed to support your cloud journey. This paper seeks to empower you to maximize value from your investments, improve forecasting accuracy and cost predictability, create a culture of ownership and cost transparency, and continuously measure your optimization status.

This paper discusses how you can automate elasticity to get the most value out of your AWS resources and optimize costs.

Introduction

In the traditional data center-based model of IT, once infrastructure is deployed, it typically runs whether it is needed or not, and all the capacity is paid for, regardless of how much it gets used. In the cloud, resources are elastic, meaning they can instantly grow or shrink to match the requirements of a specific application.

Elasticity allows you to match the supply of resources—which cost money—to demand. Because cloud resources are paid for based on usage, matching needs to utilization is critical for cost optimization. Demand includes both external usage, such as the number of customers who visit a website over a given period, and internal usage, such as an application team using development and test environments.

There are two basic types of elasticity: time-based and volume-based. Time-based elasticity means turning off resources when they are not being used, such as a development environment that is needed only during business hours. Volume-based elasticity means matching scale to the intensity of demand, whether that’s compute cores, storage sizes, or throughput.

By combining monitoring, tagging, and automation, you can get the most value out of your AWS resources and optimize costs.

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