## PRD: Adaptive Jitter Range for Downscale Check Interval

This PRD builds upon GPM-056, aiming to dynamically adjust the jitter range applied to the downscale check interval based on observed scaling behavior, optimizing resource utilization and responsiveness.

### GPM-057: Monitor Scaling Events and Collect Metrics
**Goal:** Collect data related to scaling events to inform jitter range adjustments.
**Acceptance Criteria:**
- AIVA logs timestamped records of each scale-up and scale-down event.
- The system tracks the number of scaling events within a defined time window (e.g., 5 minutes, 1 hour).
- The system tracks the average time between scaling events within the same time window.
**Files:** `aiva/scaling_monitor.py`, `aiva/config.py`
**Spawns:** Analyze collected scaling metrics to identify potential jitter range adjustments.

### GPM-058: Implement a Threshold for Scaling Event Frequency
**Goal:** Define a threshold for the frequency of scaling events that triggers an adjustment of the jitter range.
**Acceptance Criteria:**
- A configuration parameter defines the maximum acceptable number of scaling events within a specified time window.
- A configuration parameter defines the minimum acceptable time between scaling events.
- The system compares the observed scaling event frequency and average time with the defined thresholds.
**Files:** `aiva/scaling_monitor.py`, `aiva/config.py`
**Spawns:** Implement logic to automatically adjust the jitter range based on threshold breaches.

### GPM-059: Implement Automatic Jitter Range Increase
**Goal:** Automatically increase the jitter range when the scaling event frequency exceeds the defined threshold.
**Acceptance Criteria:**
- When the scaling event frequency exceeds the defined threshold, the system automatically increases the maximum jitter value by a configurable percentage.
- The increase is logged for audit purposes.
- A configuration parameter defines the maximum allowed jitter range.
**Files:** `aiva/scaling_manager.py`, `aiva/config.py`
**Spawns:** Implement logic to automatically decrease the jitter range when scaling events are infrequent.

### GPM-060: Implement Automatic Jitter Range Decrease
**Goal:** Automatically decrease the jitter range when the scaling event frequency is below a defined threshold.
**Acceptance Criteria:**
- When the scaling event frequency is below the defined threshold (indicating a lack of responsiveness), the system automatically decreases the maximum jitter value by a configurable percentage.
- The decrease is logged for audit purposes.
- The jitter range does not decrease below a configurable minimum value.
**Files:** `aiva/scaling_manager.py`, `aiva/config.py`
**Spawns:** Implement a mechanism to prevent jitter range oscillations.

### GPM-061: Implement Jitter Range Oscillation Prevention
**Goal:** Prevent rapid oscillations of the jitter range by introducing hysteresis.
**Acceptance Criteria:**
- The jitter range is only increased if the scaling event frequency exceeds the threshold for a sustained period (e.g., multiple consecutive time windows).
- The jitter range is only decreased if the scaling event frequency remains below the threshold for a sustained period.
- A configuration parameter defines the duration of the sustained period.
**Files:** `aiva/scaling_manager.py`, `aiva/config.py`
**Spawns:** Add monitoring and alerting for jitter range adjustments.

### GPM-062: Implement Monitoring and Alerting for Jitter Adjustments
**Goal:** Provide visibility into jitter range adjustments and alert administrators to unexpected behavior.
**Acceptance Criteria:**
- The system logs all jitter range adjustments, including the reason for the adjustment (e.g., exceeded scaling event frequency threshold).
- The system generates alerts when the jitter range reaches its maximum or minimum allowed value.
- The system generates alerts if the jitter range is adjusted excessively frequently (e.g., more than once per hour).
**Files:** `aiva/scaling_manager.py`, `aiva/monitoring.py`
**Spawns:** Implement a feedback loop based on real-world performance data to further refine the jitter adjustment algorithm.
