reatimemonitor

Capture Data on Cloud Server:

Traffic Features: Extract network traffic data, including key traffic metrics for analysis. HTTP Packet Data: Capture and process HTTP packets traveling through the cloud server. eBPF Kernel Data: Utilize eBPF (Extended Berkeley Packet Filter) to capture user-mode instructions transmitted through the kernel, enabling system-level monitoring. Data Streaming via Kafka:

Stream the captured data (traffic, HTTP packets, eBPF kernel data) in real-time using Kafka for efficient data flow. Data Processing with Spark EMR:

Stream the Kafka data into Spark EMR for processing. Spark will allow us to handle large-scale, distributed data processing efficiently. Reactor Pattern for Real-Time Dashboard:

Integrate a Reactor Pattern into the backend to handle the real-time data flow and push updates to a dashboard for visualization.