Elastic Load Balancing and Auto Scaling

What you will be able to do

• Explain the five core cloud decisions involved in elastic load balancing and auto scaling.
• Choose a cloud resource or configuration that matches a stated workload requirement.
• Interpret provider state, workload behavior, and operational evidence before changing broader cloud state.
• Apply an inventory, select, configure, verify, recover, and document workflow to a realistic cloud decision.

01

Build the Cloud Decision Map

Connect Elastic Load Balancing with target groups, health checks, Auto Scaling policies, and safe capacity limits.

Cloud services combine provider-managed layers with customer-controlled configuration. A useful design keeps workload need, resource scope, identity, network path, data, health, cost, and recovery visible instead of relying on a product name.

Start with an inventory and read-only evidence. Record the account or project, location, resource identifier, owner, configuration source, dependencies, expected behavior, and current observation before changing state.

02

Load balancer

Elastic Load Balancing accepts configured traffic and distributes it to registered healthy targets. It answers one distinct architecture or operating question within elastic load balancing and auto scaling.

Verify listener, protocol, port, subnet, security, and target behavior as one path. Keep the target, location, identity, configuration, and observed outcome together so another person can reproduce the reasoning.

Respect this boundary: Do not treat the load balancer endpoint as proof that targets are healthy. The expected evidence is requests reach eligible targets through the intended listener.

03

Target group

A target group defines registered destinations, routing settings, and health checks used by a load balancer. It answers one distinct architecture or operating question within elastic load balancing and auto scaling.

Register only compatible targets and verify the application path the health check uses. Keep the target, location, identity, configuration, and observed outcome together so another person can reproduce the reasoning.

Respect this boundary: Do not use a shallow health path that stays green during application failure. The expected evidence is healthy registration matches real application readiness.

04

Health check

A health check decides whether a target should receive new traffic based on repeated observations. It answers one distinct architecture or operating question within elastic load balancing and auto scaling.

Test healthy and intentionally unhealthy behavior with safe thresholds. Keep the target, location, identity, configuration, and observed outcome together so another person can reproduce the reasoning.

Respect this boundary: Do not configure thresholds that flap targets during ordinary startup. The expected evidence is the target enters and leaves service at the expected times.

05

Scaling policy

An Auto Scaling policy changes desired capacity from metrics, schedules, or predictive behavior within configured limits. It answers one distinct architecture or operating question within elastic load balancing and auto scaling.

Choose a demand signal and stabilization behavior tied to workload capacity. Keep the target, location, identity, configuration, and observed outcome together so another person can reproduce the reasoning.

Respect this boundary: Do not scale from a symptom produced by an already saturated dependency. The expected evidence is capacity changes once and settles without uncontrolled oscillation.

06

Capacity limits

Minimum, desired, and maximum capacity define the operating envelope for an Auto Scaling group. It answers one distinct architecture or operating question within elastic load balancing and auto scaling.

Test both scale-out and scale-in while protecting availability and state. Keep the target, location, identity, configuration, and observed outcome together so another person can reproduce the reasoning.

Respect this boundary: Stop when maximum capacity could overload a downstream dependency or budget. The expected evidence is the group stays within limits and preserves service health.

07

Verify One Cloud Change

Before changing cloud state, name the exact resource, required authorization, user impact, expected signal, cost effect, and recovery path. Preview the scope with an inventory or policy view and protect data or configuration that cannot be recreated safely.

Make one narrow change and stop. If the provider response, location, identity, dependency, or effective configuration differs from the plan, preserve that evidence and reassess instead of adding unrelated changes.

A successful API response or portal notification proves only that an operation was accepted. Repeat the original workload path, inspect health and security signals, verify the resulting resource state independently, and remove temporary access or test resources.

08

Recap Before Practice and Prove

Start with load balancer. Verify listener, protocol, port, subnet, security, and target behavior as one path. Confirm that requests reach eligible targets through the intended listener.

Keep target group separate. Register only compatible targets and verify the application path the health check uses. Respect this boundary: do not use a shallow health path that stays green during application failure.

Use health check as its own decision. Test healthy and intentionally unhealthy behavior with safe thresholds. Preserve the resulting evidence.

Before a broader change, review scaling policy. Choose a demand signal and stabilization behavior tied to workload capacity. Stop when do not scale from a symptom produced by an already saturated dependency.

Finish with capacity limits. Test both scale-out and scale-in while protecting availability and state. Record the final state, health signal, recovery boundary, owner, and next decision.