Tutorial / Cram Notes

Auto Scaling is a key feature in cloud computing that enables systems to automatically adjust their capacity based on the current load. This feature is vital in ensuring that applications run efficiently while keeping costs low by scaling resources up or down as needed. Amazon Web Services (AWS) provides auto-scaling capabilities across a range of its services to handle varying levels of demand. In this article, we will dive into the capabilities of Auto Scaling for different AWS services such as EC2 Auto Scaling groups, RDS storage auto scaling, DynamoDB, ECS capacity providers, and EKS autoscalers.

EC2 Auto Scaling Groups

Amazon EC2 Auto Scaling helps maintain application availability and allows users to scale Amazon EC2 capacity up or down automatically according to conditions defined for the particular application. It can be used with applications hosted on EC2 instances across multiple Availability Zones to ensure fault tolerance.

Capabilities:

  • Dynamic scaling responds to changing demand.
  • Scheduled scaling allows you to plan for predictable load changes.
  • Health checks replace or terminate instances that fail.
  • A variety of scaling policies (Target Tracking Scaling, Step Scaling, and Simple Scaling) to control how and when the service should scale.

An example of a policy that adjusts the desired capacity of an application based on the average CPU utilization is:

{
“TargetValue”: 50.0,
“PredefinedMetricSpecification”: {
“PredefinedMetricType”: “ASGAverageCPUUtilization”
},
“Cooldown”: 300,
“ResourceLabel”: “my-asg”
}

RDS Storage Auto Scaling

AWS RDS Storage Auto Scaling automatically adjusts the amount of storage space available to a database as needed, ensuring that the database workload has access to the storage it requires without manual intervention.

Capabilities:

  • Automatically scales the storage when it reaches a specified threshold.
  • Works with Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and SQL Server engines.
  • Ensures performance is not affected during scaling operations.

DynamoDB

Amazon DynamoDB is a NoSQL database service that provides fast and predictable performance with seamless scalability. With DynamoDB Auto Scaling, you’re able to automatically adjust the number of read and write capacity units that your application is provisioned with.

Capabilities:

  • Handles read and write throughput of tables or global secondary indexes.
  • Adjusts provisioned throughput based on actual application usage.
  • Uses AWS Application Auto Scaling to manage the scaling rules.

ECS Capacity Providers

Amazon Elastic Container Service (ECS) Capacity Providers manage the infrastructure scaling for containerized applications by automatically adjusting the amount of compute capacity available to tasks within a cluster.

Capabilities:

  • Supports both FARGATE and FARGATE_SPOT capacity providers.
  • Can manage scaling for ECS services based on resource requirements and availability.
  • Utilizes EC2 Auto Scaling groups to add or remove EC2 instances.

EKS Autoscalers

Amazon Elastic Kubernetes Service (EKS) allows you to run Kubernetes on AWS without needing to install or maintain your own Kubernetes control plane. It supports both Vertical Pod Autoscaler (VPA) and Horizontal Pod Autoscaler (HPA).

Capabilities:

  • VPA adjusts the CPU and memory reservations of pods.
  • HPA scales out or in the number of pod replicas based on observed CPU utilization or other select metrics.
  • Cluster Autoscaler adjusts the number of nodes in the EKS cluster based on the demands of workloads.

For each of these services, AWS provides fine-grained control over the scaling processes, allowing users to set minimum and maximum thresholds, scaling cooldown periods, and scaling policies based on a wide range of metrics. These auto-scaling capabilities ensure applications remain responsive to user demands while optimizing for cost.

In practice, developers and DevOps engineers can combine these auto-scaling services to manage complex environments where EC2 instances handle the web server load, RDS databases scale storage automatically, DynamoDB adjusts throughput on-demand, ECS Fargate manages container infrastructure, and EKS handles Kubernetes workloads—all adjusting in real-time based on usage patterns and defined policies.

Users configuring these services for a certification exam like the AWS Certified DevOps Engineer – Professional (DOP-C02) would have to demonstrate knowledge in defining and managing auto-scaling policies and understanding the nuances of how auto-scaling can affect application availability, cost, and performance.

Practice Test with Explanation

True or False: AWS EC2 Auto Scaling can only adjust the number of EC2 instances based on maximum and minimum limits defined by the user.

  • A) True
  • B) False

Answer: B) False

Explanation: AWS EC2 Auto Scaling can adjust the number of EC2 instances based on demand by using scaling policies that consider metrics such as CPU usage, network traffic, or custom metrics.

Which AWS service automatically adjusts read and write throughput capacity for your DynamoDB tables and indexes?

  • A) AWS Lambda
  • B) RDS Scaling
  • C) DynamoDB Auto Scaling
  • D) EC2 Auto Scaling

Answer: C) DynamoDB Auto Scaling

Explanation: DynamoDB Auto Scaling automatically adjusts read and write throughput capacity for your DynamoDB tables and indexes in response to actual traffic patterns.

True or False: RDS storage auto scaling can automatically increase storage size when free space is below your defined threshold but cannot decrease storage automatically when the space is not being used.

  • A) True
  • B) False

Answer: A) True

Explanation: RDS storage auto scaling can automatically increase storage when it is below a defined threshold. However, it does not decrease storage automatically to prevent data loss.

Which AWS service provides auto scaling capabilities for containerized workloads?

  • A) Amazon S3
  • B) AWS Elastic Beanstalk
  • C) Amazon ECS
  • D) Amazon EBS

Answer: C) Amazon ECS

Explanation: Amazon ECS provides auto scaling capabilities (ECS capacity providers) for containerized workloads to adjust the number of container instances based on demand.

True or False: EC2 Auto Scaling groups can maintain a fixed number of running EC2 instances even in the event of instance failures.

  • A) True
  • B) False

Answer: A) True

Explanation: EC2 Auto Scaling groups help maintain application availability by keeping a fixed number or a range of running instances, replacing instances that fail or are terminated.

Which AWS service offers automatic scaling of nodes based on the observed CPU and memory usage in a Kubernetes environment?

  • A) Amazon Route 53
  • B) Amazon RDS
  • C) Amazon ECS
  • D) Amazon EKS

Answer: D) Amazon EKS

Explanation: Amazon EKS (with Kubernetes Cluster Autoscaler) offers automatic scaling of nodes in a Kubernetes environment based on CPU and memory utilization metrics.

True or False: DynamoDB Auto Scaling uses AWS CloudWatch metrics to scale up or down your table’s provisioned throughput.

  • A) True
  • B) False

Answer: A) True

Explanation: DynamoDB Auto Scaling uses CloudWatch metrics to monitor the table’s throughput and then scales up or down the provisioned capacity as needed.

What does RDS storage auto scaling rely on to trigger a scaling event?

  • A) Network Throughput
  • B) Database Engine Version
  • C) Available Storage Space
  • D) CPU Utilization

Answer: C) Available Storage Space

Explanation: RDS storage auto scaling triggers a scaling event based on available storage space, ensuring that the database has enough space to operate efficiently.

True or False: EC2 Auto Scaling groups can only scale in response to changes in AWS CloudWatch alarms.

  • A) True
  • B) False

Answer: B) False

Explanation: Although EC2 Auto Scaling groups often use AWS CloudWatch alarms to trigger scaling events, they can also scale according to schedules, predictive scaling, or external notifications.

ECS capacity provider strategies allow which type of auto scaling?

  • A) Only scale-out actions
  • B) Only scale-in actions
  • C) Both scale-out and scale-in actions
  • D) Neither scale-out nor scale-in actions

Answer: C) Both scale-out and scale-in actions

Explanation: ECS capacity providers support both scale-out (adding more resources) and scale-in (removing excess resources) actions to match the required demand.

True or False: When using RDS storage auto scaling, there is a significant downtime when scaling up the storage.

  • A) True
  • B) False

Answer: B) False

Explanation: RDS storage auto scaling is designed to increase storage with zero downtime, allowing applications to continue to operate without interruption.

Which AWS service provides horizontal autoscaling by adding more instances, as opposed to vertical scaling like RDS storage auto scaling?

  • A) AWS Fargate
  • B) Amazon Redshift
  • C) EC2 Auto Scaling
  • D) AWS Lambda

Answer: C) EC2 Auto Scaling

Explanation: EC2 Auto Scaling provides horizontal scaling by adjusting the number of EC2 instances, while RDS storage auto scaling is an example of vertical scaling where storage space is increased instead.

Interview Questions

How do EC2 Auto Scaling groups help in maintaining application availability and how can you configure scaling policies based on conditions?

EC2 Auto Scaling helps maintain application availability by automatically adjusting the number of EC2 instances in response to the demand. You can configure scaling policies based on conditions like CPU utilization, network traffic, or custom metrics. For instance, you can create a target tracking scaling policy that maintains a desired target for a specific metric, or a step scaling policy that changes the number of EC2 instances in steps based on the size of the alarm breach.

Can you explain the difference between dynamic scaling and predictive scaling in EC2 Auto Scaling?

Dynamic scaling responds to changing demand by adjusting the capacity in real-time, using scaling policies based on specific metrics. Predictive scaling, on the other hand, uses machine learning to schedule the right number of EC2 instances based on predicted demand, ensuring that the capacity is available before traffic changes occur.

Describe how RDS storage auto-scaling works and when it should be used?

RDS storage auto-scaling automatically adjusts the amount of allocated storage space when it approaches the current limit while respecting the maximum limit set by the user. It should be used for databases with workloads that are unpredictable or that grow over time, minimizing the need for manual intervention and reducing the risk of running out of storage space.

What is the purpose of DynamoDB auto scaling and how do you enable it?

DynamoDB auto scaling adjusts the read and write capacity units of a table or index to maintain performance and manage costs in response to changing application traffic. You enable it by setting the auto scaling policies on the desired table or index, defining the minimum and maximum capacity units, and the target utilization percentage.

In the context of ECS, what is a capacity provider and how does it facilitate auto scaling?

A capacity provider in ECS is used to manage the infrastructure that the tasks will run on. It defines an association between cluster capacity (like an EC2 Auto Scaling group) and ECS services, which allows the service to scale in and out as demand changes. This provides better resource utilization and flexibility in scaling ECS services.

How can you implement auto scaling in an EKS cluster and what are the options available?

Auto scaling in EKS can be implemented using the Kubernetes Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), or the Cluster Autoscaler. HPA changes the number of pods based on CPU or memory utilization, VPA adjusts the CPU and memory reservations, and the Cluster Autoscaler adds or removes nodes based on pod scheduling requirements.

What are the advantages and potential trade-offs of using AWS Auto Scaling services in a multi-tier application architecture?

AWS Auto Scaling services offer high availability, better resource utilization, and cost efficiency by automatically adapting to traffic patterns. However, potential trade-offs include increased complexity in configuration and the potential for scaling events to cause transient performance impacts as new instances warm-up.

Can you explain how scaling plans in AWS Auto Scaling consider different resources across multiple services, such as EC2 instances, DynamoDB, and RDS?

AWS Auto Scaling allows you to create scaling plans that automate how groups of different resources respond to changes in demand. It leverages predictive scaling and dynamic scaling for EC2, and it integrates with DynamoDB and RDS auto scaling to adjust capacities across multiple services within a unified interface, simplifying scaling operations and management.

Given an example of a scaling policy that might be used for a batch processing application, how would you adjust the Auto Scaling settings if the processing job is CPU-bound?

For a CPU-bound batch processing application, you would create a target tracking scaling policy focused on the CPU utilization metric. Set the target value to the desired utilization threshold (e.g., 70%). Auto Scaling would then adjust the number of instances to maintain that target level, ensuring that there is sufficient processing power to manage the workload efficiently.

Discuss the role of cooldown periods in EC2 Auto Scaling. What is their purpose and how do they affect the scaling process?

Cooldown periods in EC2 Auto Scaling are a configurable setting that defines the amount of time to wait after a scaling activity before taking further scaling action. This is to allow newly launched instances to become fully operational and to prevent frequent scale-in or scale-out actions that might result in thrashing. It ensures that the previous activity has had time to impact the load before making additional scaling decisions.

How does AWS Fargate work with ECS Auto Scaling and what benefits does it provide?

AWS Fargate with ECS Auto Scaling allows containers to be run without having to manage the underlying instances. ECS services configured with Fargate launch type can use Auto Scaling to adjust task counts based on demand. The primary benefit is that it simplifies operations, as the need to manage servers is eliminated, while still providing the scalability and cost management features of container-based architectures.

What metrics would be critical to monitor to ensure a successful auto scaling strategy for a web application?

Critical metrics to monitor include CPU utilization, memory usage, network in/out, request count per target, and latency. Additionally, application-specific metrics such as transaction rates or queue depth could be essential. Monitoring these metrics enables an effective auto scaling strategy that ensures the application can handle varying loads while maintaining performance and cost efficiency.

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Volkan Öztonga
5 months ago

Great blog post on auto scaling in AWS! Can anyone share their experiences with EC2 Auto Scaling groups?

Deniz ArslanoÄŸlu
6 months ago

Thank you for the detailed blog post. I think I need to explore RDS storage auto scaling next.

Gonca Polat
6 months ago

I’ve had some issues with ECS capacity providers. They don’t seem to scale as expected sometimes.

مانی حیدری

This post is very helpful for preparing for the AWS Certified DevOps Engineer – Professional exam. Thanks!

Herculana Barbosa
6 months ago

What are the best practices for setting up DynamoDB auto scaling?

Séléna Laurent
6 months ago

I appreciate the blog post. It covered many aspects of auto-scaling that I was unaware of.

Sippie Koop
5 months ago

Has anyone tried using EKS autoscalers with custom metrics?

Barış Erbay
6 months ago

Can DynamoDB auto scaling handle sudden spikes in traffic?

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