## AIVA PRD: Adaptive Latency Thresholds for Scaling

This PRD focuses on improving AIVA's latency-based scaling mechanism by making the latency thresholds dynamic and responsive to system load. This will allow AIVA to adapt to changing traffic patterns and maintain optimal performance.

### GPM-079: Implement Baseline Load Measurement
**Goal:** Establish a method to measure the current system load.
**Acceptance Criteria:**
- A new metric, `system_load`, is available, representing the average CPU utilization across all AIVA instances.
- `system_load` is updated at least every 5 seconds.
- `system_load` metric can be accessed programmatically.
**Files:** `aiva/metrics/system_load.py`, `aiva/config/metrics.yaml`
**Spawns:** Integrate `system_load` into the existing latency-based scaling algorithm.

### GPM-080: Define Load-Latency Relationship
**Goal:** Define a function that relates system load to desired latency thresholds.
**Acceptance Criteria:**
- A configurable function `desired_latency(system_load)` exists.
- The function outputs target latency values (e.g., `low_latency_threshold`, `high_latency_threshold`).
- The function's parameters (e.g., slope, intercept) are configurable via a configuration file.
- The default function is a simple linear relationship.
**Files:** `aiva/scaling/latency_thresholds.py`, `aiva/config/scaling.yaml`
**Spawns:** Implement different latency threshold functions (e.g., exponential, logarithmic).

### GPM-081: Integrate Dynamic Thresholds into Scaling Algorithm
**Goal:** Modify the existing scaling algorithm to use dynamically adjusted latency thresholds.
**Acceptance Criteria:**
- The scaling algorithm uses the output of `desired_latency(system_load)` to determine scaling actions.
- Scaling actions are triggered based on the dynamically adjusted `low_latency_threshold` and `high_latency_threshold`.
- The scaling algorithm continues to function correctly under varying system load conditions.
**Files:** `aiva/scaling/scaler.py`
**Spawns:** Implement hysteresis to prevent rapid scaling fluctuations.

### GPM-082: Implement Threshold Adjustment Frequency
**Goal:** Control how frequently the latency thresholds are updated.
**Acceptance Criteria:**
- A configuration parameter `threshold_update_interval` (in seconds) is introduced.
- The `desired_latency(system_load)` function is called no more frequently than every `threshold_update_interval`.
- The default value for `threshold_update_interval` is 30 seconds.
**Files:** `aiva/config/scaling.yaml`, `aiva/scaling/scaler.py`
**Spawns:** Explore adaptive adjustment of the `threshold_update_interval` based on the rate of change of `system_load`.

### GPM-083: Monitoring and Logging of Dynamic Thresholds
**Goal:** Ensure the dynamic latency thresholds are properly monitored and logged.
**Acceptance Criteria:**
- The current values of `system_load`, `low_latency_threshold`, and `high_latency_threshold` are logged at least every 60 seconds.
- These values are also exposed as metrics that can be monitored externally.
**Files:** `aiva/metrics/metrics.py`, `aiva/scaling/scaler.py`
**Spawns:** Develop visualization tools to monitor the dynamic thresholds and their impact on scaling decisions.

### GPM-084: Testing with Simulated Load
**Goal:** Verify the dynamic threshold adjustment with simulated load patterns.
**Acceptance Criteria:**
- A test suite exists that simulates different system load scenarios (e.g., sudden spikes, gradual increases).
- The tests verify that the latency thresholds adjust appropriately in each scenario.
- The tests verify that the scaling algorithm responds correctly to the adjusted thresholds.
**Files:** `tests/scaling/test_dynamic_thresholds.py`
**Spawns:** Implement automated performance testing to evaluate the effectiveness of the dynamic thresholds under realistic load conditions.

### GPM-085: Initial Configuration Tuning
**Goal:** Determine reasonable initial values for the `desired_latency` function parameters.
**Acceptance Criteria:**
- Conduct experiments to determine suitable values for the `desired_latency` function parameters (e.g., slope, intercept).
- Document the rationale behind the chosen values in the AIVA documentation.
- Update the default configuration file with the tuned values.
**Files:** `aiva/config/scaling.yaml`, `docs/scaling.md`
**Spawns:** Implement a feedback loop to automatically tune the `desired_latency` function parameters based on observed system performance.
