Identifying and implementing appropriate auto scaling, load balancing, and caching solutions

Can you explain the difference between horizontal and vertical scaling and when you would choose one over the other in an AWS environment?

Horizontal scaling, also known as scaling out, involves adding more instances to spread the load across multiple resources, whereas vertical scaling, known as scaling up, refers to adding more power (CPU, RAM) to an existing instance. In AWS, horizontal scaling is generally preferred for distributed systems as it allows for high availability and fault tolerance by using services like EC2 Auto Scaling. You would choose vertical scaling when a single instance needs to be more powerful and the application doesn’t distribute loads well, but it has limits and can lead to a single point of failure.

How do you configure an Auto Scaling Group in AWS to maintain high availability across multiple Availability Zones?

To maintain high availability, you configure the Auto Scaling Group (ASG) to distribute instances evenly across multiple Availability Zones within a region. During setup, you specify multiple subnets, each in a different AZ. This ensures that if one AZ becomes unavailable, the others can continue to handle the load. Also, you would set up health checks and define scaling policies to replace unhealthy instances automatically.

Describe one method to ensure that your Auto Scaling activities do not exceed budget while also maintaining performance.

One method is to implement scheduled scaling to automatically scale your resources based on predictable load changes, coupled with scaling policies that include maximum and minimum limits on the number of instances in your Auto Scaling Group. AWS Budgets can also be used to set custom cost constraints and receive alerts if the estimated cost exceeds the budget.

What is the purpose of Elastic Load Balancing in AWS, and how does it work with Auto Scaling?

Elastic Load Balancing automatically distributes incoming application traffic across multiple targets, such as EC2 instances. It ensures reliability by detecting unhealthy instances and routing traffic only to healthy instances. When integrated with Auto Scaling, the load balancer can register new instances automatically as they are launched and deregister instances when they are terminated, providing a seamless scaling experience.

Discuss how you would use Amazon CloudFront and Amazon ElastiCache together to improve application performance.

Amazon CloudFront is a content delivery network (CDN) service that caches content at edge locations closest to the users, which reduces latency. Amazon ElastiCache is an in-memory caching service that allows you to cache frequently accessed data to reduce the load on databases and improve read performance. Using them together, you can cache both static and dynamic content, minimizing the response time and reducing the load on origin servers.

What factors would you consider when choosing between Amazon’s Application Load Balancer (ALB) and Network Load Balancer (NLB)?

When choosing between ALB and NLB, consider the following factors: ALB is best for HTTP/HTTPS traffic with advanced request routing, targeted at application level (Layer 7), making it suitable for managing complex content and routing rules. NLB, on the other hand, is ideal for TCP/UDP traffic and is used for extreme performance and static IP addresses for the load balancer at the connection level (Layer 4). You would choose between them based on the specific requirements of your application and the type of traffic it receives.

How can Amazon RDS Read Replicas be used in conjunction with Auto Scaling to improve database performance?

Amazon RDS Read Replicas allow you to create one or more read-only copies of your database instance to increase read throughput and failover. Auto Scaling can be used to automatically adjust the number of Read Replicas in response to changes in read traffic, ensuring that the database layer can scale out to meet demand and scale in when demand decreases, thereby improving performance and cost efficiency.

Provide an example of how to automate the synchronization of scaling activities between the application layer and the database layer.

You can automate synchronization using AWS Lambda functions triggered by CloudWatch alarms. When scaling events occur at the application layer, CloudWatch alarms can invoke Lambda functions to orchestrate corresponding scaling actions on the database layer, like adding RDS Read Replicas or adjusting DynamoDB throughput capacity, ensuring balanced scaling across the stack.

Describe how AWS’s Elasticache can be used to reduce the number of read requests to a relational database in a high traffic web application.

AWS ElastiCache can cache frequent database queries or commonly accessed items in-memory to reduce the read load on the relational database. This allows web applications to retrieve data from the fast in-memory cache rather than querying the database, which reduces latency, improves throughput, and helps the database to scale for read-intensive workloads.

In what scenarios would AWS’s Auto Scaling predictive scaling be particularly beneficial?

Predictive scaling is beneficial in scenarios where traffic patterns have predictable, cyclical trends that can be forecasted by machine learning algorithms. By analyzing historical load metrics, predictive scaling automatically schedules the right number of EC2 instances in anticipation of upcoming changes in demand. It is particularly useful for applications with daily, weekly, or seasonal variation in usage, like e-commerce websites during holidays or promotional events.