Dynamic Scaling

5 minutes 5 Questions

Dynamic Scaling is an Auto Scaling feature that automatically adjusts the number of instances in an Auto Scaling group based on the real-time demand of your application. It works by monitoring the CloudWatch alarms associated with the specific metrics such as CPU utilization, network traffic, or request latency, and then adds or removes instances accordingly to maintain an optimal balance between the demand and available resources. This ensures that your application has sufficient resources to handle the current load, while minimizing the cost and optimizing resource utilization.

AWS Solution Architect: Dynamic Scaling Guide

What is Dynamic Scaling?
Dynamic Scaling is a feature of AWS services, such as AWS Auto Scaling, that adjusts the capacity of your applications in response to workload changes. This ensures that your application can scale out to handle load spikes and scale in to reduce cost when demand decreases.

Why is it Important?
Understanding Dynamic Scaling is crucial for an AWS Solution Architect as it helps to optimize resource usage, improve application performance, and manage costs effectively.

How does it work?
Dynamic Scaling works on the principles of scaling policies which are rules that define when to scale out (add resources) or scale in (remove resources) based on metrics like CPU usage, request counts etc.

Exam Tips: Answering Questions on Dynamic Scaling
When answering questions on Dynamic Scaling:

Highlight the importance of dynamic scaling in cost-management and resource optimization.
Explain how scaling policies and alarms trigger the scaling process.
Mention the metrics which can be monitored to initiate scaling processes.
Remember: While it's important to add resources to manage increased demand, proper configuration of scale-in policies is also critical to avoid excessive cost.

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Question 1

A company needs to control costs for its dynamic scale-out process in response to unexpected high traffic. What should they use to optimize the cost during dynamic scaling?

D: Launch instances using AWS Elastic Beanstalk A: Use EC2 Spot Instances for the Auto Scaling Group B: Implement Time-based Scaling policies C: Use On-Demand instances for the Auto Scaling Group

Correct Answer: A: Use EC2 Spot Instances for the Auto Scaling Group

EC2 Spot Instances can save up to 90% compared to On-Demand instances and can be used in an Auto Scaling Group to optimize costs during dynamic scale-out events. The other options do not provide the same cost-saving opportunity.

Question 2

A mobile application backend uses auto scaling to handle varying loads. It must handle occasional short periods of high load. Which EC2 Auto Scaling policy is best suited for this scenario?

B: Scheduled Scaling Policy D: Fixed Scaling Policy A: Simple Scaling Policy C: Target Tracking Scaling Policy

Correct Answer: A: Simple Scaling Policy

In this scenario, Simple Scaling Policy provides the best solution for short periods of high load as it adjusts capacity based on simple alarm triggers. The other policies do not provide the same level of support for handling short periods of high load.

Question 3

A gaming company wants to ensure its application can handle a sudden increase in users during a new game launch. How can they achieve this using Dynamic Scaling?

C: Set up EC2 Scheduled Scaling A: Set up Amazon EC2 Auto Scaling with Target Tracking Scaling Policy D: Manually adjust Auto Scaling Group size during the launch B: Use AWS Elastic Beanstalk with a Fixed Scaling Policy

Correct Answer: A: Set up Amazon EC2 Auto Scaling with Target Tracking Scaling Policy

Dynamic Scaling is best achieved using Amazon EC2 Auto Scaling with Target Tracking Scaling Policy as it automatically scales based on demand while monitoring predefined metrics. The other options do not offer the same level of automatic, dynamic scaling.

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