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[FileCatalyst Agent] <--gRPC--> [Bandwidth Controller (new)] | v [Policy DB + Predictor Service] | v [FileCatalyst Central Server API] func AdjustBandwidth(currentLoad float64, policies []Policy) int for _, p := range policies if p.Condition.Matches(time.Now()) newLimit := p.LimitMbps if currentLoad > p.CongestionThreshold newLimit = newLimit / 2 return newLimit return defaultBandwidthMbps
Since you requested to "develop a feature," I will outline how to for FileCatalyst. Feature Overview: Intelligent Bandwidth Allocation Goal: Dynamically adjust transfer speeds based on network congestion, business priority, and historical patterns (e.g., reduce bandwidth during 9–11 AM peak business hours, ramp up overnight). 1. Core Components to Develop | Component | Description | |-----------|-------------| | Network Telemetry Collector | Monitors latency, packet loss, and jitter via FileCatalyst HotFolder or API | | Policy Engine | Allows admins to set rules (time-based, source/destination, file type) | | Predictive Scheduler | Uses historical data to pre-adjust bandwidth limits | | FileCatalyst API Integrator | Dynamically updates transfer settings without restarting transfers | 2. Step-by-Step Development Plan Step 1 – Extend FileCatalyst’s REST API FileCatalyst provides a REST API (on port 8085 for Central Server). Add custom endpoints:
-- Sample query to extract historical usage from FileCatalyst DB SELECT DATE_TRUNC('hour', start_time) as hour, AVG(transfer_rate_mbps) as avg_rate FROM filecatalyst_transfers WHERE start_time > NOW() - INTERVAL '30 days' GROUP BY hour; Use the prediction to set future bandwidth via API:
curl -X PUT http://filecatalyst-server:8085/api/transfers/config \ -H "Authorization: Bearer $API_KEY" \ -d '"max_bandwidth_mbps": 85' Add a new tab in FileCatalyst Central Web UI (customizable via plugin architecture or separate React app):
"policy_id": "peak_hrs_limit", "conditions": "time_range": "09:00-11:00", "day_of_week": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"], "source_subnet": "10.0.0.0/8" , "action": "set_bandwidth_limit_mbps": 50 , "priority": 1
# Flask microservice to proxy and augment FileCatalyst API @app.route('/api/v1/bandwidth/predict', methods=['POST']) def predict_bandwidth(): data = request.json historical_usage = get_historical_bandwidth(data['time_slot']) predicted_limit = apply_ml_model(historical_usage) update_filecatalyst_policy(predicted_limit) return "new_limit_mbps": predicted_limit Create a rule definition schema (JSON):
Train a lightweight time-series model (e.g., ARIMA or Facebook Prophet) on transfer logs:
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