Automated Network Download Analyzer

📌 Project Overview

This project implements a secure, automated client-server system using low-level TCP socket programming in Python.

Multiple clients periodically download a file from a central server, measure network performance metrics, and send the results back for analysis of congestion patterns over time.

The system simulates real-world network load conditions and helps identify performance variations and busiest network periods.

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Problem Statement

Network performance varies due to congestion, user load, and time-based usage patterns.

This project aims to:

• Automate periodic file downloads
• Measure performance metrics (speed, latency, throughput)
• Analyze trends to detect network congestion patterns

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System Architecture

🔹 Client

• Periodically downloads a test file (scheduled execution)
• Measures:
  • Download speed
  • Latency (download time)
  • Response time
• Sends metrics securely to the server using TLS

🔹 Server

• Handles multiple clients concurrently using threading
• Receives and processes client data
• Logs and aggregates performance metrics
• Uses secure SSL/TLS communication

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Security Implementation

• Uses Python ssl module for TLS encryption

Server:

• Creates SSLContext
• Loads certificate (server.crt) and key (server.key)
• Wraps socket using wrap_socket()

Client:

• Establishes secure TLS connection with server

Ensures encrypted communication over TCP

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Core Features

• Low-level TCP socket programming (AF_INET, SOCK_STREAM)
• Secure communication using TLS/SSL
• Multi-client handling using threading
• Automated periodic downloads
• Centralized performance analysis

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Metrics Measured

• Throughput (Mbps) – data transfer rate
• Latency – time taken for download completion
• Response Time – server responsiveness
• Download Speed Statistics:
  • Average speed
  • Maximum speed
  • Minimum speed
• Total downloads per client

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Performance Evaluation

• Simulates multiple concurrent clients
• Analyzes:
  • Network congestion patterns
  • Peak usage periods (busiest hour)
  • Performance variation over time

Outputs:

• Logs
• Statistical summaries
• Graphs

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Technologies Used

• Python
• TCP Socket Programming
• SSL/TLS (ssl module)
• Multithreading
• JSON / Logging

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How to Run

1. Start Server

python server.py

2. Run Client(s)

python analyser.py
python report.py

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Conclusion

This project demonstrates a secure, scalable, and automated network monitoring system that analyzes performance trends and detects congestion patterns effectively.