## AIVA: Dynamic Scaling Thresholds Based on Workload

This PRD outlines the implementation of a mechanism to dynamically adjust AIVA's scaling thresholds based on the current workload. This will allow AIVA to be more responsive and efficient in managing resources under varying conditions.

### GPM-052: Implement Workload Monitoring

**Goal:** Collect and aggregate relevant workload metrics.
**Acceptance Criteria:**
- CPU utilization is monitored and recorded at 1-minute intervals.
- Memory utilization is monitored and recorded at 1-minute intervals.
- Network I/O is monitored and recorded at 1-minute intervals.
- A dedicated component is responsible for metric collection.
- Metrics are stored in a time-series database (e.g., Prometheus).
**Files:** `aiva/monitoring/workload_monitor.py`, `aiva/config/monitoring.yaml`
**Spawns:** Implement a basic moving average calculation for workload metrics.

### GPM-053: Calculate Moving Averages for Workload Metrics

**Goal:** Implement a moving average calculation for the collected workload metrics.
**Acceptance Criteria:**
- A moving average is calculated for CPU utilization using a 5-minute window.
- A moving average is calculated for Memory utilization using a 5-minute window.
- A moving average is calculated for Network I/O using a 5-minute window.
- The moving average calculation is performed efficiently.
- The moving average values are stored along with the raw metrics.
**Files:** `aiva/processing/metric_analyzer.py`, `aiva/utils/math_utils.py`
**Spawns:** Define initial scaling threshold ranges based on workload metrics.

### GPM-054: Define Initial Scaling Threshold Ranges

**Goal:** Define initial ranges for scaling thresholds based on workload metric moving averages.
**Acceptance Criteria:**
- A configuration file defines the initial ranges for CPU, Memory, and Network I/O utilization that trigger scaling events.
- The configuration allows specifying "low," "medium," and "high" thresholds for each metric.
- Default values are provided if the configuration is missing.
- Thresholds can be adjusted without requiring code changes.
**Files:** `aiva/config/scaling_thresholds.yaml`, `aiva/scaling/threshold_manager.py`
**Spawns:** Implement a mechanism to read scaling thresholds from configuration.

### GPM-055: Implement Threshold Reading and Application

**Goal:** Read scaling thresholds from configuration and apply them to scaling logic.
**Acceptance Criteria:**
- The `threshold_manager` reads scaling threshold ranges from the configuration file.
- The current workload metrics are compared against the defined threshold ranges.
- The scaling logic uses the determined threshold ranges to trigger scaling events.
- Log messages indicate which threshold range is currently active.
**Files:** `aiva/scaling/scaler.py`, `aiva/scaling/threshold_manager.py`
**Spawns:** Implement basic scaling actions based on thresholds (e.g., log a message).

### GPM-056: Implement Basic Scaling Actions (Logging)

**Goal:** Implement basic scaling actions based on the current threshold level (log message).
**Acceptance Criteria:**
- If CPU utilization exceeds the "high" threshold, a log message is generated indicating the need to scale up.
- If CPU utilization falls below the "low" threshold, a log message is generated indicating the need to scale down.
- Similar log messages are generated for Memory and Network I/O.
- The log messages include the current metric values and threshold ranges.
**Files:** `aiva/scaling/scaler.py`
**Spawns:** Implement actual scaling actions (e.g., adjust resource allocation).

### GPM-057: Implement Scaling Action: Resource Adjustment (Dummy)

**Goal:** Implement a dummy resource adjustment action based on scaling decisions.
**Acceptance Criteria:**
- When a scale-up condition is met, a dummy function simulates increasing resource allocation (e.g., CPU cores, memory). The function logs its actions.
- When a scale-down condition is met, a dummy function simulates decreasing resource allocation. The function logs its actions.
- Resource allocation is represented by a simple integer value for testing purposes.
- This story focuses on the control flow and logging, not actual resource management.
**Files:** `aiva/scaling/scaler.py`, `aiva/resource_manager/dummy_resource_manager.py`
**Spawns:** Integrate with a real resource management system (e.g., Kubernetes API).

### GPM-058: Add Dynamic Threshold Adjustment Logic

**Goal:** Implement logic to dynamically adjust scaling thresholds based on system performance.
**Acceptance Criteria:**
- The system monitors the effectiveness of scaling actions.
- If scaling actions are not resulting in desired performance improvements (e.g., CPU utilization remains high after scaling up), thresholds are adjusted upward.
- If scaling actions are causing unnecessary resource consumption (e.g., CPU utilization is low after scaling up), thresholds are adjusted downward.
- Adjustments are gradual and limited to a maximum percentage per adjustment cycle.
**Files:** `aiva/scaling/threshold_manager.py`, `aiva/performance_analyzer/scaling_effectiveness_analyzer.py`
**Spawns:** Implement a mechanism to persist adjusted threshold values across restarts.
