## AIVA: Dynamic Threshold Adjustment - Phase 1

This PRD focuses on implementing the initial phase of dynamically adjusting scaling thresholds in AIVA based on system behavior, starting with CPU utilization.

### GPM-059: CPU Utilization Monitoring Module
**Goal:** Implement a module to collect and aggregate CPU utilization data.
**Acceptance Criteria:**
- The module collects CPU utilization data at configurable intervals (e.g., every 5 seconds, configurable via a setting).
- The module calculates a rolling average of CPU utilization over a configurable window (e.g., 1 minute, configurable via a setting).
- The module exposes the current rolling average CPU utilization via an API.
- The API is accessible from other AIVA modules.
**Files:** `aiva/monitoring/cpu_monitor.py`, `aiva/config/settings.py`
**Spawns:** Integrate the CPU utilization monitoring data with the threshold adjustment logic.

### GPM-060: Threshold Adjustment Configuration
**Goal:** Define the configuration parameters for dynamic threshold adjustment based on CPU utilization.
**Acceptance Criteria:**
- A configuration file (e.g., YAML or JSON) defines the adjustment rules based on CPU utilization ranges.
- The configuration includes parameters such as:
    - CPU utilization threshold ranges (e.g., 0-50%, 50-80%, 80-100%).
    - Scaling factor for each range (e.g., increase threshold by 10%, decrease threshold by 5%).
    - Minimum and maximum threshold values to prevent over-adjustment.
- The configuration file is loaded at AIVA startup and can be reloaded without restarting the system.
**Files:** `aiva/config/threshold_adjustment_config.yaml`, `aiva/utils/config_loader.py`
**Spawns:** Implement the logic to read the configuration and apply it to dynamically adjust scaling thresholds.

### GPM-061: Initial Threshold Adjustment Logic
**Goal:** Implement the core logic to dynamically adjust scaling thresholds based on CPU utilization and the defined configuration.
**Acceptance Criteria:**
- The module reads the current CPU utilization from the `cpu_monitor` module.
- The module evaluates the CPU utilization against the configured threshold ranges.
- Based on the matching range, the module applies the corresponding scaling factor to the scaling thresholds.
- The module respects the minimum and maximum threshold values.
- The adjusted thresholds are applied to the AIVA scaling mechanism.
**Files:** `aiva/threshold/dynamic_adjuster.py`
**Spawns:** Implement logging and monitoring for the threshold adjustment process to track changes and identify potential issues.

### GPM-062: Threshold Adjustment Logging
**Goal:** Add logging to track the threshold adjustment process.
**Acceptance Criteria:**
- Log entries are created whenever a threshold is adjusted.
- Log entries include:
    - Timestamp
    - Original threshold value
    - CPU utilization
    - Applied scaling factor
    - New threshold value
- Log level is configurable.
**Files:** `aiva/threshold/dynamic_adjuster.py`, `aiva/config/logging.yaml`
**Spawns:** Implement alerting based on threshold adjustments, such as sending notifications when thresholds are adjusted beyond a certain range.

### GPM-063: Integration Testing of Dynamic Threshold Adjustment
**Goal:** Create integration tests to verify the dynamic threshold adjustment functionality.
**Acceptance Criteria:**
- Tests simulate different CPU utilization scenarios.
- Tests verify that the thresholds are adjusted correctly according to the configuration.
- Tests verify that the minimum and maximum threshold values are respected.
- Tests verify that logging is working correctly.
**Files:** `tests/integration/test_dynamic_threshold_adjustment.py`
**Spawns:** Implement a feedback mechanism to monitor the impact of threshold adjustments on system performance and adapt the adjustment rules accordingly.
