Tutorial / Cram Notes
Before diving into scaling factors, let’s briefly review the types of load balancers AWS offers:
- Classic Load Balancer (CLB): Operates at both the request level and connection level and is ideal for simple load balancing of traffic across multiple EC2 instances.
- Application Load Balancer (ALB): Operates at the application layer (Layer 7) offering advanced routing, load balancing to multiple HTTP applications, and containers running on the same instance.
- Network Load Balancer (NLB): Operates at the transport layer (Layer 4), suitable for high-throughput, low-latency needs, or for TCP traffic where extreme performance is required.
Autoscaling with AWS Load Balancers
AWS Load Balancers can be scaled manually or automatically based on predefined conditions using AWS Auto Scaling. Auto Scaling helps maintain application availability and allows you to scale your Amazon EC2 capacity up or down automatically according to conditions you define.
Scaling Factors for Load Balancers
Traffic Patterns
- Predictable patterns: You can schedule scaling activities based on predictable load changes, such as peak usage times.
- Unpredictable bursts: Use predictive scaling policies to handle unexpected traffic surges. This ensures that the load balancer can accommodate the increase in load without degradation in performance.
Health Check Configuration
Load balancer health checks ensure that traffic is only sent to healthy instances. When configuring health checks:
- Adjust the health check intervals and thresholds to account for the increase or decrease in the number of instances.
- Be mindful of the health check grace period after new instances are launched.
Connection Draining
Connection draining enables the load balancer to complete in-flight requests before deregistering or terminating instances. When scaling down:
- Ensure that the connection draining timeout is configured properly to prevent the termination of instances that are processing requests.
Elastic Load Balancing (ELB) Capacity
- Pre-warming: Inform AWS when expecting a traffic spike, so they can “pre-warm” the load balancer to handle the load.
- Request Rates: If the number of new connections or requests per second increases, you may need to scale your load balancer to accommodate the additional load.
Other Considerations
- Resource limits: Monitor your account’s service limits (e.g., the number of load balancers, max number of targets registered) and request an increase if needed.
- Latency: Monitor and log latency metrics. High latency might indicate the need for scaling.
- Instance type: For EC2-backed applications, ensure that the instance type and size can accommodate the expected load after scaling.
Scaling in Action: Example
Suppose you have an ALB distributing traffic across an Auto Scaling group of EC2 instances. You predict a traffic increase since a promotional event is scheduled. In your AWS Auto Scaling configuration, you set up a target tracking scaling policy to maintain an average CPU utilization of 50%. As traffic begins to ramp up, CPU utilization rises, and the Auto Scaling group automatically launches new EC2 instances. These new instances are then registered with the ALB, which immediately begins distributing traffic to them, maintaining performance and handling the increased load efficiently.
Additionally, if you expect a significant surge in traffic, you can pre-warm the ALB by contacting AWS Support and providing details such as expected request rates and response sizes.
In conclusion, when scaling load balancers in AWS, it’s crucial to consider factors such as traffic patterns, healthy instance management, resource limits, and the performance of your instances. Proper Auto Scaling policies and configurations will ensure a resilient and cost-effective infrastructure capable of responding to dynamic load conditions.
Practice Test with Explanation
True/False: AWS Elastic Load Balancing automatically scales its request-handling capacity in response to incoming application traffic.
- A) True
- B) False
Answer: A) True
Explanation: AWS Elastic Load Balancer (ELB) automatically adjusts its scaling capacity based on incoming application traffic, ensuring it can handle varying load levels without manual intervention.
Which AWS service is primarily used for distributing traffic across multiple targets, such as EC2 instances, in multiple Availability Zones?
- A) Amazon Route 53
- B) AWS Elastic Load Balancer
- C) AWS Auto Scaling
- D) AWS Direct Connect
Answer: B) AWS Elastic Load Balancer
Explanation: AWS Elastic Load Balancer is designed to automatically distribute incoming application traffic across multiple targets, such as Amazon EC2 instances, containers, IP addresses, and Lambda functions in multiple Availability Zones.
True/False: Network Load Balancer (NLB) operates at Layer 4 of the OSI model and cannot handle encrypted traffic (HTTPS/SSL).
- A) True
- B) False
Answer: B) False
Explanation: While it is true that the Network Load Balancer operates at Layer 4 of the OSI model, it can handle encrypted traffic as it supports TLS listeners.
Which attribute should be adjusted if an Application Load Balancer is not scaling properly due to slow target response times?
- A) Idle timeout
- B) Unhealthy threshold
- C) Desired capacity
- D) Health check interval
Answer: A) Idle timeout
Explanation: The ‘idle timeout’ setting on an Application Load Balancer controls the time a connection can be idle before it is closed. Adjusting this may help with scaling issues related to slow target response times.
True/False: Scaling policies in AWS Auto Scaling are only triggered by changes in EC2 instance metrics.
- A) True
- B) False
Answer: B) False
Explanation: AWS Auto Scaling can use a variety of metrics from different AWS services, not just EC2, to trigger scaling policies. These could include metrics from Amazon RDS, Amazon DynamoDB, and Custom CloudWatch metrics.
When should you consider using a Classic Load Balancer over an Application Load Balancer?
- A) When you need path-based routing
- B) When you require support for sticky sessions
- C) When you have simple load-balancing of TCP traffic
- D) When you require advanced request routing based on headers
Answer: C) When you have simple load-balancing of TCP traffic
Explanation: A Classic Load Balancer is best suited for simple load balancing of TCP traffic, whereas an Application Load Balancer is used for more complex routing requirements such as path-based routing and routing based on headers.
True/False: AWS recommends using a fixed number of EC2 instances behind a load balancer to ensure consistent performance.
- A) True
- B) False
Answer: B) False
Explanation: AWS recommends using Auto Scaling in conjunction with a load balancer to automatically adjust the number of EC2 instances in response to varying load, rather than using a fixed number of instances.
Which scaling policy type in AWS Auto Scaling adjusts the desired capacity based on the aggregate of all instance metrics?
- A) Target Tracking Scaling
- B) Step Scaling
- C) Simple Scaling
- D) Scheduled Scaling
Answer: A) Target Tracking Scaling
Explanation: Target Tracking Scaling adjusts the desired capacity based on a defined target value for a specific aggregate metric. This approach simplifies the scaling process by automating the adjustments needed to maintain the target metric.
True/False: Sticky sessions should be enabled if you want session data to be bound to a single EC2 instance for the life of the session.
- A) True
- B) False
Answer: A) True
Explanation: Sticky sessions bind user sessions to a specific EC2 instance, ensuring that all requests from a user during the session are directed to the same instance, which is useful for retaining session information locally on the instance.
What is the primary benefit of using an Elastic Load Balancer with a multi-AZ deployment?
- A) Reduced latency
- B) Enhanced security
- C) High availability
- D) Cost savings
Answer: C) High availability
Explanation: An Elastic Load Balancer in conjunction with a multi-AZ deployment provides high availability by distributing traffic across instances in multiple Availability Zones, which can withstand failure of an entire AZ.
Interview Questions
What is the primary function of a load balancer in AWS?
The primary function of a load balancer in AWS is to automatically distribute incoming application traffic across multiple targets, such as EC2 instances, containers, IP addresses, and Lambda functions. This improves the availability and scalability of the application.
Which AWS load balancer type automatically scales its request handling capacity in response to incoming application traffic?
All AWS load balancers (Application Load Balancer, Network Load Balancer, and Classic Load Balancer) are designed to automatically scale their request handling capacity according to incoming traffic. AWS ensures that the load balancers can handle the demand by adjusting the resources they use.
How does an Application Load Balancer (ALB) determine the need for scaling?
An Application Load Balancer determines the need for scaling based on request load and other metrics like CPU and network I/O utilization. AWS continuously monitors these metrics and adjusts the ALB resources automatically to meet demand.
What is the difference in scaling capacity between a Classic Load Balancer and a Network Load Balancer (NLB)?
A Classic Load Balancer operates at Layer 4, providing basic load balancing across EC2 instances. Its scaling capacity is smaller and slower compared to an NLB, which operates at Layer 4 as well but is optimized for high throughput, low latency, and scales more rapidly and massively due to its use of Elastic Network Interfaces (ENIs).
How can you monitor the scaling activity of an AWS load balancer?
You can monitor the scaling activity of an AWS load balancer using Amazon CloudWatch, which provides metrics such as request count, latency, and target health status. You can use these metrics to understand traffic patterns and the performance of the load balancer.
What is the role of health checks in the scaling behavior of load balancers?
Health checks are used by load balancers to ensure that traffic is only routed to healthy and available targets. If a target is unhealthy, the load balancer stops sending traffic to it, which may trigger scaling activities to launch new instances or containers to handle the load if integrated with Auto Scaling.
When configuring an Auto Scaling group with a load balancer, what scaling policies might you use to accommodate varying loads?
When configuring an Auto Scaling group with a load balancer, you might use policies such as target tracking, step scaling, or simple scaling policies. These enable you to scale based on specific metrics like CPU utilization or network input/output, or based on CloudWatch alarms linked to load balancer metrics.
Can scaling policies for load balancers account for predictable traffic patterns, and if so, how?
Yes, scaling policies can account for predictable traffic patterns by using scheduled scaling actions. AWS allows you to schedule actions to increase or decrease the number of instances in your Auto Scaling group at specific times in response to predictable load changes.
In a high-traffic scenario, what might be a limitation when using an Application Load Balancer in terms of scaling?
In a high-traffic scenario, one potential limitation of an Application Load Balancer might be the time it takes to scale out. While the ALB can handle sudden increases in traffic, it may take a short time to provision additional load balancing capacity. Planning and proactive scaling can help minimize potential disruptions.
Are there any AWS services or features that specifically help with pre-warming a load balancer before an expected traffic spike?
Yes, AWS offers pre-warming services for load balancers. If you expect a traffic spike, you can contact AWS Support to request “pre-warming” for your load balancer, which prepares it to handle the traffic surge smoothly by scaling up proactively.
What metric should you primarily focus on when setting up an Auto Scaling policy associated with an NLB for a latency-sensitive application?
For a latency-sensitive application using an NLB, the primary focus should be on the “TargetResponseTime” metric when setting up Auto Scaling policies. This metric ensures that the number of instances or containers is adjusted based on the application’s response time requirements.
How does AWS Global Accelerator complement the scaling abilities of load balancers?
AWS Global Accelerator complements the scaling abilities of load balancers by directing user traffic to the optimal endpoint based on performance, which can improve the overall efficiency of how the load is distributed among your services. This can help load balancers respond effectively to varying traffic loads by working with the most optimal network paths.
Great blog post on load balancers and scaling factors! It was really helpful for my ANS-C01 exam prep.
Can someone explain the primary differences between scaling up and scaling out for load balancers?
Thanks for the detailed explanation! I have better clarity now.
How do Auto Scaling Groups interact with load balancers in AWS?
Appreciate the insights!
What are the limitations of using Elastic Load Balancers (ELB) on AWS?
This was really useful, thanks!
Is there a rule of thumb for determining the scaling thresholds for an Application Load Balancer (ALB)?