## AIVA PRD: Dynamic Threshold Adjustment Based on CPU Utilization

This PRD focuses on integrating the CPU utilization data collected by the CPU Utilization Monitoring Module (GPM-059) with the threshold adjustment logic within AIVA. This integration will allow AIVA to dynamically adjust thresholds for various monitored metrics based on the current CPU load, optimizing resource allocation and preventing false positives during periods of high CPU usage.

### GPM-060: CPU Utilization Data Access in Threshold Adjustment Module
**Goal:** Make CPU utilization data accessible to the threshold adjustment module.
**Acceptance Criteria:**
- The threshold adjustment module can successfully retrieve the average CPU utilization over the last 5 minutes from the CPU utilization monitoring module.
- Error handling is implemented in case the CPU utilization monitoring module is unavailable or returns invalid data.
**Files:** `aiva/threshold_adjustment/threshold_adjuster.py`, `aiva/data_access/cpu_utilization_data.py` (interface to CPU utilization module)
**Spawns:** Develop a basic threshold adjustment algorithm that incorporates CPU utilization.

### GPM-061: Basic CPU-Aware Threshold Adjustment Algorithm
**Goal:** Implement a basic algorithm to adjust thresholds based on CPU utilization.
**Acceptance Criteria:**
- When CPU utilization exceeds 75%, all thresholds are increased by 10%.
- When CPU utilization is below 25%, all thresholds are decreased by 5%.
- The algorithm is configurable, allowing modification of the CPU utilization thresholds (75% and 25%) and percentage adjustments (10% and 5%).
**Files:** `aiva/threshold_adjustment/threshold_adjuster.py`
**Spawns:** Implement more sophisticated threshold adjustment strategies based on CPU utilization, considering specific metrics and their sensitivity to CPU load.

### GPM-062: Metric-Specific CPU-Aware Threshold Adjustment
**Goal:** Refine the threshold adjustment algorithm to consider the specific metric being monitored and its sensitivity to CPU load.
**Acceptance Criteria:**
- A configuration file allows specifying which metrics are sensitive to CPU load and to what extent.
- Metrics designated as highly sensitive have their thresholds adjusted more aggressively than metrics designated as less sensitive.
- Metrics not designated as sensitive are not adjusted based on CPU utilization.
**Files:** `aiva/threshold_adjustment/threshold_adjuster.py`, `aiva/config/metric_sensitivity.yaml` (or similar)
**Spawns:** Implement hysteresis to prevent excessive threshold fluctuations.

### GPM-063: Threshold Adjustment Hysteresis
**Goal:** Implement hysteresis in the threshold adjustment mechanism to prevent rapid and frequent threshold changes due to small fluctuations in CPU utilization.
**Acceptance Criteria:**
- A hysteresis window of 5% is implemented for CPU utilization. Thresholds are only adjusted if CPU utilization crosses the upper or lower thresholds (e.g., 75% and 25%) and remains above or below them for at least 1 minute.
- The hysteresis window and duration are configurable.
**Files:** `aiva/threshold_adjustment/threshold_adjuster.py`
**Spawns:** Add logging and monitoring for threshold adjustments.

### GPM-064: Logging and Monitoring of Threshold Adjustments
**Goal:** Implement logging and monitoring to track threshold adjustments made by the system.
**Acceptance Criteria:**
- Each threshold adjustment event is logged, including the metric being adjusted, the original threshold, the new threshold, the CPU utilization at the time of adjustment, and the reason for the adjustment.
- Key threshold adjustment metrics (e.g., frequency of adjustments, magnitude of adjustments) are exposed for monitoring.
**Files:** `aiva/threshold_adjustment/threshold_adjuster.py`, `aiva/monitoring/threshold_adjustment_metrics.py`
**Spawns:** Implement a feedback mechanism to learn and adapt the threshold adjustment algorithm based on past performance.

### GPM-065: Test the CPU-aware threshold adjustments
**Goal:** Create automated tests for the functionality implemented in previous stories.
**Acceptance Criteria:**
- Unit tests should cover the threshold adjustment algorithm with various CPU utilizations and metric sensitivities.
- Integration tests should simulate a scenario with increasing CPU load and verify that thresholds are adjusted accordingly.
**Files:** `tests/aiva/threshold_adjustment/test_threshold_adjuster.py`
**Spawns:** Develop a feedback mechanism to learn and adapt the threshold adjustment algorithm based on past performance.
