## AIVA PRD: Implement Infrastructure Scaling via API

This PRD focuses on enabling AIVA to perform actual infrastructure scaling by integrating with cloud provider APIs. It builds upon the successful simulation of scale-up actions (GPM-057) by replacing the simulated CPU allocation increase with real API calls to scale resources in a cloud environment.

### GPM-058: Configure Cloud Provider Credentials

**Goal:** Allow AIVA to securely store and retrieve cloud provider credentials.
**Acceptance Criteria:**
- A mechanism exists to securely store cloud provider credentials (e.g., AWS Access Key ID and Secret Access Key, Azure Subscription ID and Client Secret).
- AIVA can retrieve these credentials during runtime.
- Support for at least one cloud provider (AWS, Azure, or GCP) is implemented.
**Files:** `config/cloud_credentials.py`, `utils/credential_manager.py`
**Spawns:** Implement authentication with the chosen cloud provider's API.

### GPM-059: Implement AWS EC2 Scaling

**Goal:** Enable AIVA to scale AWS EC2 instances using the AWS SDK (Boto3).
**Acceptance Criteria:**
- AIVA can authenticate with AWS using the stored credentials.
- AIVA can increase the number of EC2 instances in an Auto Scaling Group.
- AIVA can specify the Auto Scaling Group to scale.
**Files:** `cloud_providers/aws_scaling.py`
**Spawns:** Implement scaling for other resource types (e.g., CPU within an instance, storage) in AWS.

### GPM-060: Implement Azure Virtual Machine Scaling

**Goal:** Enable AIVA to scale Azure Virtual Machines using the Azure SDK.
**Acceptance Criteria:**
- AIVA can authenticate with Azure using the stored credentials.
- AIVA can increase the number of Virtual Machine Scale Set instances.
- AIVA can specify the Virtual Machine Scale Set to scale.
**Files:** `cloud_providers/azure_scaling.py`
**Spawns:** Implement scaling for other resource types (e.g., CPU within an instance, storage) in Azure.

### GPM-061: Integrate Scaling Logic with CPU Utilization Threshold

**Goal:** Connect the infrastructure scaling logic to the CPU utilization threshold monitoring.
**Acceptance Criteria:**
- When the CPU utilization exceeds the configured threshold (inherited from GPM-057), AIVA triggers the appropriate scaling action (AWS or Azure based on configuration).
- Scaling actions are logged with timestamps and success/failure status.
**Files:** `core/scaling_manager.py`, `core/monitoring.py` (modified)
**Spawns:** Implement scaling down actions when utilization drops below a lower threshold.

### GPM-062: Implement Basic Error Handling for Scaling Operations

**Goal:** Implement basic error handling for scaling API calls.
**Acceptance Criteria:**
- AIVA handles API call failures gracefully (e.g., network errors, invalid credentials, resource limits).
- Errors are logged with sufficient detail for debugging.
- A mechanism exists to retry scaling operations (with a configurable retry count and delay).
**Files:** `utils/error_handling.py`, `cloud_providers/aws_scaling.py` (modified), `cloud_providers/azure_scaling.py` (modified)
**Spawns:** Implement more sophisticated error handling and alerting (e.g., sending notifications to administrators).

### GPM-063: Add Configuration Options for Scaling Parameters

**Goal:** Allow users to configure scaling parameters, such as the increment size and the scaling target.
**Acceptance Criteria:**
- Users can configure the number of instances to add during a scale-up event.
- Users can specify the Auto Scaling Group (AWS) or Virtual Machine Scale Set (Azure) to scale via a configuration file.
- Default values are provided for all configurable parameters.
**Files:** `config/aiva_config.yaml` (modified), `core/config_loader.py` (modified)
**Spawns:** Implement dynamic scaling policies based on historical data or predictive models.
