A website uses EC2 instances with Auto Scaling and EFS. How can the company optimize costs?

Using a target tracking scaling policy is a primary method for cost optimization in Amazon EC2 Auto Scaling. This policy automatically adjusts the number of EC2 instances to maintain a specific metric, such as average CPU utilization, at a target value you define. This ensures that you are not over-provisioned with idle instances during periods of low demand, nor under-provisioned during high demand. By dynamically matching the compute capacity to the actual workload, this approach minimizes waste and directly optimizes operational costs, aligning with the AWS Well-Architected Framework's principle of matching supply with demand.

A. Setting a fixed number of instances eliminates elasticity, leading to over-provisioning (wasted cost) or under-provisioning (poor performance), which is the opposite of cost optimization.

B. Using larger instances (vertical scaling) is not inherently a cost optimization strategy and could significantly increase costs if the additional capacity is not consistently utilized.

D. Replacing persistent, shared EFS with ephemeral instance stores is architecturally unsound for an Auto Scaling group, as it would lead to data loss when instances are terminated.

1. AWS Auto Scaling User Guide: "With target tracking scaling policies

you select a scaling metric and set a target value... Amazon EC2 Auto Scaling creates and manages the CloudWatch alarms that trigger the scaling policy and calculates the scaling adjustment based on the metric and target value. The scaling policy adds or removes capacity as required to keep the metric at

or close to

the specified target value."

Source: AWS Auto Scaling User Guide

"Target tracking scaling policies for Amazon EC2 Auto Scaling".

2. AWS Well-Architected Framework - Cost Optimization Pillar: This approach directly implements the design principle "Matching supply and demand." The framework states

"A best practice for cloud architecture is to provision the resources to match your demand. Over-provisioning resources results in unnecessary cost

and under-provisioning results in a poor customer experience."

Source: AWS Well-Architected Framework

Cost Optimization Pillar

Design Principles

"Matching supply and demand" section. (Document ID: aws-well-architected-framework.pdf

Page 4).

3. Amazon EC2 User Guide for Linux Instances: This document clarifies the ephemeral nature of instance stores: "Data on an instance store volume persists only during the life of the associated instance. If an instance is stopped

hibernated

or terminated

any data on its instance store volumes is lost." This confirms why option D is incorrect for a stateful or shared-data application using Auto Scaling.

Source: Amazon EC2 User Guide for Linux Instances

"Amazon EC2 instance store"

section "Instance store lifetime".