Add scaling usecase

docs/getting_started/scaling_resources.md

@@ -1,8 +1,10 @@
+
 ---
 title: Autoscaling Resources
 ---
 
-Autoscaling allows organizations to dynamically manage compute resources based on demand, optimizing efficiency and cost. 
+ClearML provides the options to automate your resource scaling, while optimizing machine usage.
+Autoscaling allows you to dynamically manage compute resources based on demand, optimizing efficiency and cost. 
 
 When running machine learning experiments or large-scale compute tasks, demand for resources fluctuates. Autoscaling ensures that:
 - **Resources are available when needed**, preventing delays in task execution.
@@ -10,37 +12,54 @@ When running machine learning experiments or large-scale compute tasks, demand f
 - **Workloads can be distributed efficiently**.
 
 ClearML offers the following resource autoscaling solutions:
-* Built-in GUI applications - Built-in applications to autoscale, no code required (available under the Pro and Enterprise plans)
+* [GUI applications](#gui-autoscaler-applications) (available under the Pro and Enterprise plans) -  Use the built-in apps to define your compute 
+  resource budget, and have the apps automatically manage your resource consumption as needed–with no code! 
   * AWS Autoscaler
   * GCP Autoscaler
-* Kubernetes autoscaling
-* Custom autoscaler implementation using the `AutoScaler` class
+* [Kubernetes integration](#kubernetes-integration) - Deploy agents in Kubernetes for automated resource allocation and scaling
+* [Custom autoscaler implementation](#custom-autoscaler-implementation) using the `AutoScaler` class
 
 ### GUI Autoscaler Applications
-For users on **Pro** and **Enterprise** plans, ClearML provides a UI applications to configure autoscaling for cloud 
+For users on Pro and Enterprise plans, ClearML provides a UI applications to configure autoscaling for cloud 
 resources. These applications include:
-* [**AWS Autoscaler**](../webapp/applications/apps_aws_autoscaler.md): Automatically provisions and shuts down AWS EC2 instances based on workload demand.
-* [**GCP Autoscaler**](../webapp/applications/apps_gcp_autoscaler.md): Manages Google Cloud instances dynamically according to defined budgets.
+* [AWS Autoscaler](../webapp/applications/apps_aws_autoscaler.md): Automatically provisions and shuts down AWS EC2 instances based on workload demand.
+* [GCP Autoscaler](../webapp/applications/apps_gcp_autoscaler.md): Manages Google Cloud instances dynamically according to defined budgets.
 
 These applications allow users to set up autoscaling with minimal configuration, defining compute budgets and resource limits directly through the UI.
 
-### Kubernetes Autoscaling
-ClearML integrates with **Kubernetes**, allowing agents to be deployed within a cluster. Kubernetes handles:
-- Automatic pod creation for executing tasks.
-- Resource allocation and scaling based on workload.
-- Optional integration with Kubernetes' **Cluster Autoscaler**, which adjusts the number of nodes dynamically.
+### Kubernetes Integration
 
-This is particularly useful for organizations already using Kubernetes for workload orchestration.
+You can install `clearml-agent` through a Helm chart.
+
+ClearML integrates with Kubernetes, allowing agents to be deployed within a cluster. Kubernetes handles:
+* Automatic pod creation for executing tasks.
+* Resource allocation and scaling based on workload.
+* Optional integration with Kubernetes' cluster autoscaler, which adjusts the number of nodes dynamically.
+
+The Clearml Agent deployment is set to service a queue(s). When tasks are added to the queues, the agent pulls the task 
+and creates a pod to execute the task. Kubernetes handles resource management. Your task pod will remain pending until 
+enough resources are available.
+
+You can set up Kubernetes' cluster autoscaler to work with your cloud providers, which automatically adjusts the size of 
+your Kubernetes cluster as needed; increasing the amount of nodes when there aren't enough to execute pods and removing
+underutilized nodes. See charts for specific cloud providers.
+
+For more information, see [ClearML Kubernetes Agent](https://github.com/clearml/clearml-helm-charts/tree/main/charts/clearml-agent).
+
+:::note Enterprise features
+The ClearML Enterprise plan supports K8S servicing multiple ClearML queues, as well as providing a pod template for each 
+queue for describing the resources for each pod to use. See [ClearML Helm Charts](https://github.com/clearml/clearml-helm-charts/tree/main).
+:::
 
 ### Custom Autoscaler Implementation
-Users can build their own autoscaler using the `clearml.automation.auto_scaler.AutoScaler` class which enables:
+Users can build their own autoscaler using the [`clearml.automation.auto_scaler.AutoScaler`](https://github.com/clearml/clearml/blob/master/clearml/automation/auto_scaler.py#L77) class which enables:
 * Direct control over instance scaling logic.
 * Custom rules for resource allocation.
-* Budget-conscious decision-making based on predefined policies.
 
-This method requires some scripting.
+An `AutoScaler` instance monitors ClearML task queues and dynamically adjusts the number of cloud instances based on workload demand.
+By integrating with a [CloudDriver](https://github.com/clearml/clearml/blob/master/clearml/automation/cloud_driver.py#L62), 
+it supports multiple cloud providers like AWS and GCP.
 
-$$$$$See the AWS Autoscaler Example to see the `AutoScaler` class in action. This script can be adjusted to scale GCP resources 
-as well
+See the [AWS Autoscaler Example](../guides/services/aws_autoscaler.md) for a practical implementation using the 
+AutoScaler class. The script can be adapted for GCP autoscaling as well.
 
-demonstrates how to use the clearml.automation.auto_scaler module to implement a service that optimizes AWS EC2 instance scaling according to a defined instance budget