network-quality-assessment

🚀 Hardware timestamping vs software measurement

Problem

Software-based measurements are affected by OS scheduling, buffering and interrupt latency.

Solution

Use FPGA/SFP datapath timestamping with custom SLA probe packets.

Result

• microsecond-level jitter visibility
• accurate one-way delay
• reliable SLA validation

---

⚡ What you get

• true network delay (not host delay)
• real jitter (not OS noise)
• packet loss on datapath
• correlation-ready metrics

---

🚀 Quick navigation

• Architecture
• Packet flow
• Benchmark & analytics
• Measurement credibility
• Engineering metrics
• SLA report template
• Experiment manifests
• Case study
• Network analysis
• Real-time system
• Root cause analysis
• Clock synchronization
• Hardcore engineering
• Quick start
• Demo

---

🧭 Architecture

---

🔁 Packet flow

---

🌐 Topology

---

⚙️ FPGA timestamp pipeline

Hardware timestamp datapath

---

📦 Packet decode (Wireshark-style)

---

📊 Benchmark & analytics

SLA dashboard

---

🧪 Case study

👉 Metro Ethernet SLA validation

---

🌐 Network analysis

• 👉 Network effects
• 👉 Traffic analysis
• 👉 Anomaly detection

---

🧠 Root cause analysis

👉 Root cause analysis rules

---

⚡ Real-time system

👉 Real-time pipeline

---

📡 Monitoring integration

👉 Monitoring integration

---

⏱️ Clock synchronization

👉 PTP / clock sync

---

🧠 Hardcore engineering

• 👉 Timing error budget
• 👉 Latency model
• 👉 Measurement credibility
• 👉 Engineering metrics
• 👉 SLA report template
• 👉 Timestamp comparison manifest

---

🔬 Quick start

Generate demo dataset and graphs:

python tools/generate_demo_benchmark.py

---

🎯 Demo

👉 Run demo scenario 👉 View demo report 👉 Executive summary 👉 SLA demo manifest 👉 Software vs hardware timestamp manifest

Example output

Packet loss: 0.02%   PASS
Delay:       0.384ms PASS
Jitter:      42us    PASS
Throughput:  941Mb/s PASS

Flow

probe -> timestamp -> metrics -> detection -> report

---

📜 License

MIT