# PRODUCT REQUIREMENTS DOCUMENT: AIVA - Initial Scaling Thresholds

This PRD outlines the requirements for implementing initial scaling threshold ranges based on workload metrics calculated using moving averages (as per GPM-053).

## Stories

### GPM-054: Define Initial Threshold Configuration Schema
**Goal:** Define the data structure for configuring scaling thresholds based on workload metrics. This structure should be flexible enough to accommodate different metrics and scaling policies.
**Acceptance Criteria:**
- A JSON schema is defined that includes:
    - Metric name (e.g., "CPU Utilization", "Request Latency")
    - Threshold type (e.g., "High", "Low")
    - Threshold value (numeric)
    - Scaling action (e.g., "Scale Up", "Scale Down")
    - Cooldown period (in seconds)
- The schema is documented with examples for different workload metrics.
**Files:** `config/scaling_thresholds.schema.json`, `docs/scaling_thresholds_schema.md`
**Spawns:** Implement loading and validation of the defined threshold configuration.

### GPM-055: Implement Threshold Configuration Loading and Validation
**Goal:** Implement the functionality to load the scaling threshold configuration from a file and validate it against the defined schema.
**Acceptance Criteria:**
- A function is created that loads the scaling threshold configuration from a JSON file.
- The loaded configuration is validated against the `scaling_thresholds.schema.json` schema.
- Error handling is implemented to catch invalid configuration files and report errors with specific details about validation failures.
**Files:** `aiva/config_loader.py`, `tests/test_config_loader.py`
**Spawns:** Implement the core logic for comparing workload metrics against the configured thresholds.

### GPM-056: Implement Workload Metric Threshold Comparison Logic
**Goal:** Implement the core logic to compare the moving average of workload metrics against the configured scaling thresholds.
**Acceptance Criteria:**
- A function is created that takes a workload metric and its moving average as input.
- The function iterates through the configured scaling thresholds.
- The function compares the moving average against the threshold value based on the threshold type ("High", "Low").
- The function returns a list of scaling actions to be taken based on the threshold breaches.
**Files:** `aiva/threshold_analyzer.py`, `tests/test_threshold_analyzer.py`
**Spawns:** Implement the execution of the scaling actions triggered by threshold breaches.

### GPM-057: Logging of Threshold Breaches and Scaling Actions
**Goal:** Implement logging for threshold breaches and the resulting scaling actions.
**Acceptance Criteria:**
- Log messages are generated when a threshold is breached.
- Log messages include the metric name, threshold type, threshold value, moving average, and the scaling action triggered.
- Log messages are generated when a scaling action is successfully executed.
- Logging level is configurable.
**Files:** `aiva/logger.py`, `aiva/threshold_analyzer.py` (modified)
**Spawns:** Implement mechanisms to prevent rapid scaling and flapping.

### GPM-058: Initial Threshold Configuration File
**Goal:** Create an initial, sample configuration file for scaling thresholds based on CPU utilization and request latency.
**Acceptance Criteria:**
- A `scaling_thresholds.json` file is created with example configurations for:
    - Scaling up when CPU utilization exceeds 80% for 5 minutes (moving average).
    - Scaling down when CPU utilization is below 20% for 10 minutes (moving average).
    - Scaling up when request latency exceeds 500ms for 2 minutes (moving average).
    - Scaling down when request latency is below 100ms for 5 minutes (moving average).
- The file is well-formatted and adheres to the `scaling_thresholds.schema.json` schema.
**Files:** `config/scaling_thresholds.json`
**Spawns:** Implement a mechanism to dynamically adjust thresholds based on observed system behavior.

### GPM-059: Integrate Threshold Analysis into the AIVA Main Loop
**Goal:** Integrate the threshold analysis logic into the main AIVA loop to continuously monitor workload metrics and trigger scaling actions.
**Acceptance Criteria:**
- The `threshold_analyzer.py` is called within the AIVA main loop.
- Scaling actions returned by the threshold analyzer are executed (currently logged).
- The frequency of threshold analysis is configurable.
**Files:** `aiva/main.py` (modified)
**Spawns:** Implement the actual scaling actions (e.g., calling an API to adjust the number of instances).
