## Product Requirements Document: Adaptive Scaling Enhancement - Load-Aware Latency Adjustment

This PRD outlines the next steps in enhancing AIVA's adaptive scaling algorithm by incorporating system load measurements alongside existing latency metrics. This will allow AIVA to make more informed scaling decisions, preventing over-scaling in low-latency, high-load scenarios and under-scaling in high-latency, low-load scenarios.

### GPM-080: Fetch System Load Data
**Goal:** Collect CPU and memory utilization metrics from the AIVA system.
**Acceptance Criteria:**
- Can retrieve CPU utilization percentage as a float between 0.0 and 100.0.
- Can retrieve memory utilization percentage as a float between 0.0 and 100.0.
- Metrics are retrieved at a configurable interval (default: 1 second).
- Implement error handling to gracefully manage situations where system load data is unavailable.
**Files:** `aiva/monitoring/system_metrics.py`
**Spawns:** Develop a data structure to hold latency and load data.

### GPM-081: Create Latency and Load Data Structure
**Goal:** Define a data structure to store latency and system load information.
**Acceptance Criteria:**
- Data structure must include latency (in milliseconds), CPU utilization (percentage), and memory utilization (percentage).
- Data structure must include a timestamp for when the data was recorded.
- Data structure should be easily serializable (e.g., to JSON) for debugging and monitoring purposes.
**Files:** `aiva/scaling/data_models.py`
**Spawns:** Implement a mechanism to feed the collected system load data into the latency data stream.

### GPM-082: Integrate Load Data into Latency Stream
**Goal:** Incorporate system load data into the existing latency data stream used by the scaling algorithm.
**Acceptance Criteria:**
- System load data is appended to or associated with each latency measurement.
- The combined latency and load data is passed to the scaling algorithm.
- Existing scaling algorithm functions still operate without breaking due to the added data.
**Files:** `aiva/scaling/adaptive_scaler.py`, `aiva/monitoring/system_metrics.py`
**Spawns:** Define a weighting mechanism for latency and load within the scaling decision.

### GPM-083: Define Load Weighting Parameters
**Goal:** Introduce configurable parameters to weigh the influence of latency and system load on scaling decisions.
**Acceptance Criteria:**
- Introduce parameters (e.g., `latency_weight`, `cpu_weight`, `memory_weight`) that determine the relative importance of each metric.
- Ensure these parameters are configurable via a configuration file or environment variables.
- Default weights should be set to sensible values (e.g., latency = 0.7, CPU = 0.2, Memory = 0.1).
**Files:** `aiva/config/scaling_config.py`
**Spawns:** Implement the load-aware scaling logic.

### GPM-084: Implement Load-Aware Scaling Logic
**Goal:** Modify the scaling algorithm to consider both latency and system load when determining scaling actions.
**Acceptance Criteria:**
- The scaling algorithm uses the defined weights to calculate a combined metric from latency, CPU utilization, and memory utilization.
- Scaling decisions are based on this combined metric, triggering scale-up or scale-down events as needed.
- The system scales down less aggressively when latency is high but system load is low.
- The system scales up more aggressively when latency is high and system load is high.
**Files:** `aiva/scaling/adaptive_scaler.py`
**Spawns:** Implement logging for scaling decisions based on the new combined metric.

### GPM-085: Implement Scaling Decision Logging
**Goal:** Log the factors leading to each scaling decision, including latency, CPU utilization, memory utilization, and the resulting scaling action.
**Acceptance Criteria:**
- Log messages include the timestamp, latency, CPU utilization, memory utilization, combined metric value, and the scaling action taken (scale up, scale down, no change).
- Log messages are easily searchable and filterable for debugging and analysis.
- Logging level is configurable.
**Files:** `aiva/scaling/adaptive_scaler.py`, `aiva/utils/logger.py`
**Spawns:** Develop a testing framework for the load-aware scaling algorithm.

### GPM-086: Create Load-Aware Scaling Testing Framework
**Goal:** Develop a testing framework to validate the behavior of the load-aware scaling algorithm under various load conditions.
**Acceptance Criteria:**
- The framework should simulate different latency and load scenarios.
- The framework should automatically verify that the scaling algorithm makes appropriate decisions in each scenario.
- Tests should cover edge cases, such as extremely high latency/low load and extremely low latency/high load.
**Files:** `tests/scaling/test_adaptive_scaler.py`, `aiva/testing/simulation_tools.py`
**Spawns:** Implement initial tests for the load-aware scaling algorithm.

### GPM-087: Implement Baseline Load-Aware Scaling Tests
**Goal:** Write initial unit and integration tests for the load-aware scaling algorithm.
**Acceptance Criteria:**
- Tests cover a range of latency and load combinations.
- Tests verify that scaling decisions are consistent with the defined weights and thresholds.
- Tests are automated and repeatable.
**Files:** `tests/scaling/test_adaptive_scaler.py`
**Spawns:** Implement a feedback loop to automatically adjust the weighting parameters based on system performance.
