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# PRD: Dynamic Hysteresis Threshold Adjustment for AIVA

This PRD focuses on dynamically adjusting the hysteresis threshold for AIVA's autoscaling mechanism based on workload patterns. This builds upon the recently implemented separate upscaling and downscaling thresholds (GPM-056) to provide a more responsive and efficient scaling behavior.

### GPM-057: Implement Initial Workload Pattern Detection
**Goal:** Detect basic workload patterns (e.g., stable, increasing, decreasing, fluctuating) based on recent resource utilization data.
**Acceptance Criteria:**
- AIVA can classify workload patterns into at least four categories: "Stable", "Increasing", "Decreasing", and "Fluctuating".
- The classification algorithm uses a configurable window of recent resource utilization data (e.g., CPU usage, memory usage).
- The classification algorithm has adjustable sensitivity parameters.
- The classification algorithm should log its classification decisions and the data it used.
**Files:** `aiva/autoscaling/workload_analyzer.py`, `aiva/config/autoscaling.yaml`
**Spawns:** Explore more sophisticated workload pattern recognition techniques, potentially incorporating time series analysis.

### GPM-058: Define Hysteresis Threshold Adjustment Strategies
**Goal:** Define different strategies for adjusting the downscaling hysteresis threshold based on detected workload patterns.
**Acceptance Criteria:**
- At least three distinct adjustment strategies are defined (e.g., "Aggressive Downscale", "Conservative Downscale", "Balanced").
- Each strategy maps specific workload patterns to adjustments in the downscaling hysteresis threshold (e.g., "Increasing" -> "Conservative Downscale").
- Strategies are configurable via the AIVA configuration file.
**Files:** `aiva/autoscaling/threshold_adjuster.py`, `aiva/config/autoscaling.yaml`
**Spawns:** Investigate machine learning approaches to learn optimal threshold adjustment strategies.

### GPM-059: Implement Basic Threshold Adjustment Logic
**Goal:** Implement the core logic to adjust the downscaling hysteresis threshold based on the selected strategy and detected workload pattern.
**Acceptance Criteria:**
- The system selects a threshold adjustment strategy from the configuration file.
- The `threshold_adjuster.py` module receives workload pattern classifications from `workload_analyzer.py`.
- The `threshold_adjuster.py` module adjusts the downscaling hysteresis threshold according to the selected strategy.
- The adjusted threshold is used by the autoscaling mechanism.
**Files:** `aiva/autoscaling/threshold_adjuster.py`
**Spawns:** Implement a mechanism to dynamically switch between different threshold adjustment strategies.

### GPM-060: Log Adjusted Hysteresis Thresholds
**Goal:** Log the adjusted downscaling hysteresis thresholds and the reasons for the adjustments.
**Acceptance Criteria:**
- Every time the downscaling hysteresis threshold is adjusted, a log entry is created.
- The log entry includes: timestamp, workload pattern, selected strategy, original threshold, adjusted threshold, and justification for the adjustment.
**Files:** `aiva/autoscaling/threshold_adjuster.py`
**Spawns:** Implement real-time monitoring and visualization of the adjusted hysteresis thresholds.

### GPM-061: Configure Initial Adjustment Strategies
**Goal:** Populate the configuration file with reasonable initial values for the adjustment strategies.
**Acceptance Criteria:**
- The `aiva/config/autoscaling.yaml` file contains default configurations for the defined adjustment strategies.
- These default configurations map workload patterns to specific threshold adjustments.
- The default configurations provide a balanced approach, avoiding overly aggressive or conservative downscaling.
**Files:** `aiva/config/autoscaling.yaml`
**Spawns:** Create a UI to allow users to easily configure the threshold adjustment strategies.

### GPM-062: Implement Gradual Threshold Adjustments
**Goal:** Instead of abruptly changing the downscaling hysteresis threshold, implement gradual adjustments over time.
**Acceptance Criteria:**
- The `threshold_adjuster.py` module adjusts the threshold gradually over a configurable time period.
- The rate of adjustment is configurable.
- This prevents sudden scaling events due to rapid threshold changes.
**Files:** `aiva/autoscaling/threshold_adjuster.py`, `aiva/config/autoscaling.yaml`
**Spawns:** Explore adaptive adjustment rates based on the magnitude of the required threshold change.

### GPM-063: Add Unit Tests for Workload Analyzer
**Goal:** Add comprehensive unit tests for the `workload_analyzer.py` module.
**Acceptance Criteria:**
- Unit tests cover different workload patterns (stable, increasing, decreasing, fluctuating).
- Unit tests verify the accuracy of the workload pattern classification algorithm.
- Unit tests cover edge cases and invalid input scenarios.
**Files:** `aiva/tests/test_workload_analyzer.py`, `aiva/autoscaling/workload_analyzer.py`
**Spawns:** Implement integration tests to verify the interaction between the workload analyzer and the threshold adjuster.
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