AWS Auto Scaling is a critical component within the Amazon Web Services (AWS) ecosystem that enables users to automatically adjust the number of EC2 (Elastic Compute Cloud) instances and other resources in response to real-time demand. One of the fundamental concepts in AWS Auto Scaling that users need to configure is the "desired capacity" or "desired instances." Understanding how to set and use desired instances is essential for optimizing the cost and performance of applications running on AWS.

Understanding Desired Instances

The "desired capacity" in an Auto Scaling group refers to the ideal number of EC2 instances that the group should attempt to maintain. This parameter dictates how many instances are running at any given time, balancing between the minimum and maximum capacity limits you've set.

Key Functions and Importance

1. Load Management: The desired instances setting helps manage application load. By maintaining the appropriate number of instances, it ensures that your application handles incoming traffic efficiently, thus maintaining optimal performance.
2. Cost Efficiency: By dynamically adjusting the number of running instances, AWS Auto Scaling groups help optimize costs. Maintaining the desired number of instances prevents over-provisioning and under-utilization of resources.
3. Redundancy and High Availability: Setting an appropriate desired capacity is crucial for redundancy and maintaining service availability. It ensures that there are enough instances to handle failovers in the case of an unexpected instance termination.

Technical Operations

• Scaling In and Out: When traffic demand increases beyond the capacity of the currently running instances (falling below desired capacity), the Auto Scaling group will launch new instances to match the desired capacity. Conversely, if demand decreases and the instance count exceeds the desired capacity, the group will terminate instances to maintain the set desired capacity.
• Auto Scaling Policies: Desired instances can be modified automatically by creating scaling policies. These policies can be based on metrics such as CPU utilization, allowing dynamic adjustments based on system performance indicators.

Example Scenario

Suppose you have a web application experiencing fluctuating demand. You configure an Auto Scaling group with:

• Minimum capacity: 2 instances
• Maximum capacity: 10 instances
• Desired capacity: 5 instances
• During low traffic periods, the Auto Scaling group maintains the desired capacity of 5 instances.
• If CPU utilization exceeds a predefined threshold for all instances, a scale-out policy could increase the desired capacity to 8.
• When traffic decreases, a scale-in policy reduces the desired capacity back to 5 to save costs.

Limitations and Considerations

While desired instances help in managing costs and performance, there are several considerations and limitations:

• Capacity Shortages: AWS cannot launch new instances if the desired capacity exceeds resource quotas or available limits in a region.
• Regional Settings: Desired instances may behave differently based on whether the instances are spread across multiple availability zones.
• Policy Conflicts: Misconfigured scaling policies can cause a conflict between desired capacity and other set policies like minimum or maximum capacity.

Metrics for Adjusting Desired Capacity

AWS provides several CloudWatch metrics that can assist in deciding the desired capacity, including:

• CPU Utilization: Helps determine if more resources are required.
• NetworkIn and NetworkOut: Monitors inbound and outbound traffic.
• Request Count: Critical for applications that are request-intensive.

Summary Table

Here's a concise look at desired instances in an AWS Auto Scaling group:

By understanding and configuring the desired instances, AWS users can achieve balanced resource utilization, improving application performance while minimizing costs. The optimal configuration will depend on the specific needs and architecture of the application being deployed.