3.1.2.1. Appropriate Metrics for Scaling Services

First Principle: Appropriate metrics provide objective data for informed scaling decisions and operational excellence, enabling dynamic capacity adjustment to meet demand, preventing performance degradation, and optimizing cost.

Key metrics indicating system load and bottlenecks:

• CPU Utilization: High CPU signals compute-bound processes, a common trigger for EC2 Auto Scaling.
• Memory Usage: High RAM consumption can precede performance issues, though less direct for auto-scaling.
• Network I/O: Spikes indicate heavy traffic, prompting scaling of network-intensive services.
• Request Count (e.g., ALB RequestCount): Directly reflects application demand, used to scale web servers.
• Queue Length (e.g., SQS ApproximateNumberOfMessagesVisible): Growing queues signal processing backlog, triggering worker instance scaling.

Key Metrics for Scaling Decisions:

• Compute: CPU Utilization, Memory Usage.
• Network: Network I/O.
• Application/Service Specific: Request Count (ALB), Queue Length (SQS).

Scenario: A DevOps team manages a web application behind an ALB that processes asynchronous tasks via an SQS queue. They need to configure Auto Scaling for both the web servers and the worker instances that process the queue.

Reflection Question: How would you select and use appropriate metrics (e.g., ALB RequestCount for web servers, SQS ApproximateNumberOfMessagesVisible for workers) to drive dynamic scaling decisions for both components of this application, ensuring optimal performance and cost efficiency?

These metrics directly inform AWS Auto Scaling policies, enabling thresholds (e.g., "CPU > 70%") for automatic capacity adjustments, ensuring elastic and cost-efficient resource management.

💡 Tip: Consider custom metrics (e.g., active user sessions) for precise, application-specific scaling.