# PRD: Adaptive Hysteresis Monitoring and Refinement

This PRD focuses on implementing comprehensive logging and monitoring for the adaptive hysteresis feature, allowing for data-driven refinement of its parameters and ensuring its effectiveness in a production environment.

### GPM-065: Implement Basic Hysteresis Duration Logging
**Goal:** To log the duration of each hysteresis application (both scaling up and scaling down) for analysis.
**Acceptance Criteria:**
- Logs include the start and end timestamps of each hysteresis application.
- Logs include the duration of each hysteresis application in seconds.
- Logs are tagged with relevant metadata (e.g., scaling direction, resource type).
**Files:** `aiva/scaling_manager.py`, `aiva/logging_config.py`
**Spawns:** Analyze logged hysteresis durations to identify patterns and potential outliers.

### GPM-066: Implement Metrics for Hysteresis Frequency
**Goal:** To track how frequently hysteresis is applied in both scaling directions.
**Acceptance Criteria:**
- Prometheus metrics are exposed for "hysteresis_up_count" and "hysteresis_down_count".
- Metrics are updated each time hysteresis is applied.
- Metrics include labels for resource type and any other relevant dimensions.
**Files:** `aiva/scaling_manager.py`, `aiva/metrics.py`
**Spawns:** Correlate hysteresis frequency with resource utilization and application performance.

### GPM-067: Integrate Hysteresis Impact Monitoring with Scaling Decisions
**Goal:** To correlate hysteresis application with subsequent scaling decisions.
**Acceptance Criteria:**
- Logs indicate whether a scaling decision was influenced by hysteresis (e.g., "Hysteresis prevented scaling up").
- Logs include the resource utilization level at the time of the scaling decision.
- Logs include the scaling decision that was ultimately made.
**Files:** `aiva/scaling_manager.py`
**Spawns:** Develop automated alerts based on hysteresis behavior and scaling decision outcomes.

### GPM-068: Create a Grafana Dashboard for Hysteresis Monitoring
**Goal:** To visualize hysteresis duration, frequency, and impact on scaling decisions.
**Acceptance Criteria:**
- A Grafana dashboard displays trends in hysteresis duration over time.
- The dashboard shows the frequency of hysteresis application for different resource types.
- The dashboard visualizes the correlation between hysteresis application and scaling decisions.
**Files:** `grafana/hysteresis_dashboard.json`
**Spawns:** Investigate anomalies identified in the Grafana dashboard.

### GPM-069: Implement Alerting for Excessive Hysteresis Duration
**Goal:** To trigger alerts when hysteresis is applied for unusually long periods.
**Acceptance Criteria:**
- An alert is triggered if hysteresis duration exceeds a configurable threshold (e.g., 30 minutes).
- The alert includes information about the affected resource, the scaling direction, and the hysteresis duration.
- Alerting mechanism is integrated with existing monitoring infrastructure (e.g., Prometheus Alertmanager).
**Files:** `aiva/alerting.py`, `aiva/config.py`
**Spawns:** Develop automated remediation actions based on hysteresis alerts.

### GPM-070: Add Configuration for Hysteresis Logging Level
**Goal:** Allow administrators to configure the verbosity of hysteresis-related logs.
**Acceptance Criteria:**
- A configuration option is available to set the logging level for hysteresis-related messages (e.g., DEBUG, INFO, WARN).
- The logging level can be adjusted without requiring a restart.
- Log messages are filtered based on the configured logging level.
**Files:** `aiva/config.py`, `aiva/logging_config.py`
**Spawns:** Implement dynamic adjustment of hysteresis parameters based on real-time performance data.

### GPM-071: Implement Unit Tests for Hysteresis Logging
**Goal:** To ensure the correctness and robustness of the hysteresis logging implementation.
**Acceptance Criteria:**
- Unit tests cover different scenarios, including successful hysteresis application, failed hysteresis application, and different logging levels.
- Unit tests verify that the correct log messages are generated for each scenario.
- Code coverage for hysteresis logging is at least 80%.
**Files:** `tests/test_scaling_manager.py`
**Spawns:** Implement integration tests to validate the interaction between hysteresis and other AIVA components.
