## Product Requirements Document: AIVA Hysteresis Optimization Through Log Analysis

This PRD outlines the next steps in optimizing AIVA's frequency scaling decisions by analyzing the logs generated in GPM-051. The focus is on leveraging the logged data to refine the hysteresis parameters, aiming for a more stable and efficient frequency scaling behavior.

### GPM-052: Parse and Aggregate Frequency Scaling Logs
**Goal:**  Create a script to parse the frequency scaling logs and aggregate them into a usable format for analysis.
**Acceptance Criteria:**
- A script exists that can read the frequency scaling log files.
- The script can extract relevant data points (CPU load, frequency, thresholds, duration, decision outcome).
- The script can aggregate the extracted data into a CSV or JSON format.
- The output format is documented.
**Files:** `scripts/log_parser.py`, `docs/log_format.md`
**Spawns:** Visualize aggregated log data to identify potential issues.

### GPM-053: Visualize Frequency Scaling Decisions Over Time
**Goal:** Create visualizations to show frequency scaling decisions and relevant metrics (CPU Load, Frequency) over time.
**Acceptance Criteria:**
- A visualization tool (e.g., Matplotlib, Seaborn, Plotly) is selected.
- A script generates time-series plots of CPU load, frequency, and frequency scaling decisions.
- Plots clearly show the relationship between CPU load, frequency changes, and scaling thresholds.
- Plots are saved to a file.
**Files:** `scripts/visualization.py`, `visualizations/frequency_scaling.png`
**Spawns:** Develop metrics to quantify hysteresis performance.

### GPM-054: Define Hysteresis Performance Metrics
**Goal:** Define quantitative metrics to evaluate the effectiveness of the current hysteresis settings.
**Acceptance Criteria:**
- Metrics are defined to measure:
    - Number of frequency oscillations (up and down).
    - Duration of oscillations.
    - Frequency scaling decision delay (time between load change and frequency adjustment).
    - Energy consumption related to frequency scaling.
- The rationale behind each metric is documented.
**Files:** `docs/hysteresis_metrics.md`
**Spawns:** Implement calculation of hysteresis performance metrics.

### GPM-055: Implement Hysteresis Performance Metric Calculation
**Goal:** Implement scripts to calculate the defined hysteresis performance metrics from the aggregated log data.
**Acceptance Criteria:**
- Scripts are implemented to calculate all defined hysteresis performance metrics.
- The scripts use the output of the log parsing script (GPM-052).
- The scripts output the calculated metrics in a clear and accessible format (e.g., CSV, JSON).
- Unit tests are written to verify the correctness of the metric calculations.
**Files:** `scripts/metrics_calculation.py`, `tests/test_metrics_calculation.py`
**Spawns:** Analyze the calculated metrics to identify opportunities for optimization.

### GPM-056: Analyze Hysteresis Metrics and Identify Optimization Opportunities
**Goal:** Analyze the calculated hysteresis metrics to identify areas where the hysteresis parameters can be improved.
**Acceptance Criteria:**
- The calculated metrics are analyzed for a specific workload/benchmark.
- A report is generated summarizing the metric values and identifying potential optimization opportunities (e.g., reducing oscillations, reducing decision delay).
- The report includes specific recommendations for adjusting hysteresis parameters (e.g., increasing/decreasing the hysteresis window).
**Files:** `reports/hysteresis_analysis.md`
**Spawns:** Implement a script to automatically adjust the hysteresis parameters based on the analysis.

### GPM-057: Implement Hysteresis Parameter Adjustment Script
**Goal:** Create a script that takes recommendations from the hysteresis analysis and automatically adjusts the hysteresis parameters in AIVA.
**Acceptance Criteria:**
- A script is implemented that can modify the hysteresis parameters in AIVA's configuration files or runtime parameters.
- The script takes recommendations as input (e.g., a configuration file specifying new hysteresis values).
- The script includes error handling to prevent invalid parameter values.
- The script is documented.
**Files:** `scripts/hysteresis_adjust.py`
**Spawns:** Integrate the hysteresis adjustment script with the analysis pipeline for automated optimization.

### GPM-058: Test the Hysteresis Adjustment Script
**Goal:** Thoroughly test the hysteresis adjustment script to ensure it functions correctly and doesn't introduce unintended side effects.
**Acceptance Criteria:**
- Unit tests are written to verify that the script correctly modifies the hysteresis parameters.
- Integration tests are performed to ensure that the adjusted parameters have the desired effect on AIVA's frequency scaling behavior.
- Tests are performed with a variety of workloads and system configurations.
**Files:** `tests/test_hysteresis_adjust.py`
**Spawns:** Create a closed-loop optimization system for hysteresis parameters.
