## Predictive Rate Limiting for AIVA

This PRD outlines the implementation of predictive rate limiting based on historical resource utilization patterns to improve AIVA's performance and responsiveness.

### GPM-069: Analyze Historical Resource Utilization Data
**Goal:** Analyze historical AIVA resource utilization data to identify patterns and correlations.
**Acceptance Criteria:**
- A script exists that can extract relevant resource utilization data (CPU, memory, network) from existing logs.
- The script can identify peak usage times, recurring patterns, and correlations between different resource types.
- A report is generated summarizing the identified patterns and correlations.
**Files:** `scripts/analyze_resource_usage.py`, `reports/resource_usage_analysis.md`
**Spawns:** Develop a model to predict future resource utilization based on the analysis.

### GPM-070: Develop Resource Utilization Prediction Model
**Goal:** Develop a predictive model that forecasts future resource utilization based on historical data.
**Acceptance Criteria:**
- A model (e.g., time series forecasting, machine learning model) is trained on the historical resource utilization data.
- The model's accuracy is evaluated using a held-out dataset.
- The model can predict resource utilization for a specified time window (e.g., next hour, next day).
- The model's performance metrics (e.g., RMSE, MAE) are documented.
**Files:** `models/resource_prediction_model.py`, `notebooks/model_training.ipynb`, `docs/model_performance.md`
**Spawns:** Integrate the prediction model into the rate limiting system.

### GPM-071: Integrate Prediction Model with Rate Limiting System
**Goal:** Integrate the resource utilization prediction model into AIVA's rate limiting system.
**Acceptance Criteria:**
- The prediction model can be accessed and queried by the rate limiting module.
- The rate limiting module can dynamically adjust rate limits based on the model's predictions.
- The integration does not introduce significant latency to the rate limiting process.
**Files:** `aiva/rate_limiting/predictive_rate_limiter.py`, `aiva/rate_limiting/rate_limiter_interface.py` (modified)
**Spawns:** Implement dynamic rate limit adjustments based on predicted utilization.

### GPM-072: Implement Dynamic Rate Limit Adjustments
**Goal:** Implement the logic to dynamically adjust rate limits based on the predicted resource utilization.
**Acceptance Criteria:**
- The system can increase rate limits when predicted resource utilization is low.
- The system can decrease rate limits when predicted resource utilization is high.
- The adjustment logic includes configurable thresholds for increasing and decreasing rate limits.
- Rate limit adjustments are logged (following GPM-068 conventions).
**Files:** `aiva/rate_limiting/predictive_rate_limiter.py` (modified)
**Spawns:** Implement a mechanism to monitor and adapt the prediction model based on real-time performance.

### GPM-073: Test Predictive Rate Limiting in a Staging Environment
**Goal:** Thoroughly test the predictive rate limiting system in a staging environment to ensure stability and performance.
**Acceptance Criteria:**
- The system is deployed in a staging environment that mimics the production environment.
- Load tests are conducted to simulate realistic user traffic patterns.
- Resource utilization metrics are monitored during load tests to verify that the system is responding appropriately.
- The system's behavior under different load conditions (e.g., sudden spikes in traffic) is evaluated.
- No regressions are introduced in existing functionality.
**Files:** `tests/integration/rate_limiting_test.py`, `deployment/staging/rate_limiting_config.yaml`
**Spawns:** Implement real-time model monitoring and adaptation.

### GPM-074: Implement Real-time Model Monitoring and Adaptation
**Goal:** Implement a system to monitor the performance of the prediction model in real-time and adapt it to changing conditions.
**Acceptance Criteria:**
- The system can track the accuracy of the prediction model in real-time.
- The system can detect when the model's performance degrades below a predefined threshold.
- The system can automatically retrain the model on the most recent data when performance degrades.
- Alerts are generated when the model is retrained.
**Files:** `aiva/monitoring/model_monitoring.py`, `aiva/training/model_retraining.py`
**Spawns:** Implement anomaly detection to identify unexpected resource utilization patterns.

### GPM-075: Deploy Predictive Rate Limiting to Production
**Goal:** Deploy the predictive rate limiting system to the production environment.
**Acceptance Criteria:**
- The system is deployed to the production environment without disrupting existing services.
- Rate limiting is monitored to ensure that it is functioning correctly.
- The system is monitored for performance and stability.
- A rollback plan is in place in case of issues.
**Files:** `deployment/production/rate_limiting_config.yaml`, `docs/deployment_guide.md`
**Spawns:** Analyze the impact of predictive rate limiting on overall system performance and user experience.
