## PRD: Prioritized Network Traffic Handling During Idle

This PRD focuses on prioritizing network traffic during AIVA's idle state to ensure critical data is handled efficiently, even when the system is not actively processing user requests. This will improve responsiveness to high-priority events and reduce latency for time-sensitive information.

### GPM-045: Define Network Traffic Priority Levels
**Goal:** Define a clear hierarchy of network traffic priority levels for AIVA.
**Acceptance Criteria:**
- A document outlining at least three priority levels (e.g., High, Medium, Low) exists.
- Each priority level is clearly defined with examples of the types of data that fall under each level (e.g., real-time sensor data = High, background model updates = Low).
- The priority levels are documented in a central, accessible location.
**Files:** `docs/network_priority_levels.md`
**Spawns:** Implement Traffic Classification Based on Priority

### GPM-046: Implement Traffic Classification Based on Priority
**Goal:** Implement a mechanism to classify outgoing network traffic based on the defined priority levels.
**Acceptance Criteria:**
- AIVA can tag outgoing network packets with the appropriate priority level.
- The classification mechanism is configurable through a central configuration file.
- Unit tests verify that packets are correctly classified based on their associated data type.
**Files:** `aiva/network/traffic_classifier.py`, `aiva/config/network.yaml`, `tests/aiva/network/test_traffic_classifier.py`
**Spawns:** Implement Prioritized Queuing for Network Transmission

### GPM-047: Implement Prioritized Queuing for Network Transmission
**Goal:** Implement a queuing system that prioritizes network traffic based on its classification.
**Acceptance Criteria:**
- AIVA utilizes a queuing system for outgoing network traffic.
- Higher priority packets are placed at the front of the queue and transmitted first.
- The queuing system can handle backpressure from the network adapter.
- Unit tests verify the correct prioritization of packets within the queue.
**Files:** `aiva/network/traffic_queue.py`, `tests/aiva/network/test_traffic_queue.py`
**Spawns:** Integrate Prioritized Traffic Shaping

### GPM-048: Integrate Prioritized Traffic Shaping
**Goal:** Integrate traffic shaping to control the rate of network traffic transmission based on priority levels.
**Acceptance Criteria:**
- AIVA implements traffic shaping to limit the bandwidth usage of low-priority traffic during idle periods.
- Traffic shaping is configurable, allowing administrators to adjust bandwidth limits for each priority level.
- Monitoring tools are available to track the effectiveness of traffic shaping.
**Files:** `aiva/network/traffic_shaper.py`, `aiva/config/network.yaml`, `aiva/monitoring/network.py`
**Spawns:** Dynamic Priority Adjustment Based on System Load

### GPM-049: Monitor Network Traffic and Priority Levels
**Goal:** Provide monitoring and logging of network traffic and the effectiveness of prioritization.
**Acceptance Criteria:**
- AIVA logs the volume of traffic for each priority level.
- Metrics are available to track the latency for high-priority packets.
- These metrics can be visualized and alerted on.
**Files:** `aiva/monitoring/network.py`, `docs/monitoring_network_traffic.md`
**Spawns:** Anomaly Detection for Network Traffic Patterns

### GPM-050: Test Prioritized Network Handling with Simulated High-Priority Events
**Goal:** Verify that the prioritized network handling system functions correctly during simulated high-priority events.
**Acceptance Criteria:**
- A test suite simulates a sudden influx of high-priority network traffic.
- The test suite verifies that high-priority traffic is transmitted with minimal latency, even during periods of heavy background activity.
- The tests verify that low-priority traffic is throttled appropriately during high-priority events.
**Files:** `tests/aiva/network/test_prioritized_traffic.py`
**Spawns:** Refine Priority Levels Based on Real-World Performance Data
