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Case Studies

Real-world test results with detailed methodology.

Note: All tests are reproducible. Commands and configurations are provided for each case study.

Case Study 1: Mobile CDN Performance

Problem

A content delivery network (CDN) experiences high latency and packet loss on mobile networks, leading to poor user experience.

Hypothesis

QUIC's 0-RTT resumption and improved loss recovery should reduce latency by ~30% compared to TCP.

Methodology

Test Environment:

  • Client: Ubuntu 22.04, 4-core CPU, 8GB RAM
  • Server: Ubuntu 22.04, 8-core CPU, 16GB RAM
  • Network: Emulated 4G LTE profile

Network Profile (4G LTE):

RTT: 50-150ms (avg 80ms)
Bandwidth: 5-50 Mbps (avg 20 Mbps)
Packet Loss: 0.1-2% (avg 0.5%)
Jitter: 10-30ms (avg 15ms)

Test Commands:

# Server
docker run -p 4433:4433/udp mlanies/quic-test:latest \
  --mode=server \
  --prometheus-port=9090

# Client (QUIC)
docker run mlanies/quic-test:latest \
  --mode=client \
  --server=<server-ip>:4433 \
  --profile=mobile \
  --duration=300s \
  --streams=10 \
  --data-size=100MB

# Client (TCP for comparison)
docker run mlanies/quic-test:latest \
  --mode=client \
  --server=<server-ip>:4433 \
  --profile=mobile \
  --compare-tcp \
  --duration=300s \
  --streams=10 \
  --data-size=100MB

Results

Metric TCP QUIC Improvement
Avg RTT 95ms 62ms -35%
P95 RTT 180ms 110ms -39%
P99 RTT 250ms 145ms -42%
Throughput 18.2 Mbps 19.8 Mbps +9%
Packet Loss 0.52% 0.48% -8%
Connection Time 245ms 85ms -65% (0-RTT)

Key Findings:

  1. 0-RTT resumption dramatically reduces connection time
  2. Better loss recovery improves RTT under packet loss
  3. Head-of-line blocking elimination improves throughput

Reproduction

# Clone repository
git clone https://github.com/twogc/quic-test
cd quic-test

# Run automated test
./scripts/case-studies/mobile-cdn.sh

# Results saved to: results/mobile-cdn-YYYY-MM-DD.json

Case Study 2: Video Streaming (Satellite Link)

Problem

Video streaming over satellite links suffers from high latency (500-700ms RTT) and frequent rebuffering.

Hypothesis

QUIC's multiplexing without head-of-line blocking should reduce rebuffering by ~60%.

Methodology

Test Environment:

  • Client: Raspberry Pi 4 (ARM64)
  • Server: AWS EC2 t3.medium
  • Network: Emulated satellite profile

Network Profile (Satellite):

RTT: 500-700ms (avg 600ms)
Bandwidth: 1-10 Mbps (avg 5 Mbps)
Packet Loss: 0.5-5% (avg 2%)
Jitter: 50-100ms (avg 70ms)

Test Commands:

# Server
docker run -p 4433:4433/udp mlanies/quic-test:latest \
  --mode=server \
  --dashboard

# Client (Video simulation: 10 streams, 5 Mbps each)
docker run mlanies/quic-test:latest \
  --mode=client \
  --server=<server-ip>:4433 \
  --profile=satellite \
  --duration=600s \
  --streams=10 \
  --data-size=500MB \
  --compare-tcp

Results

Metric TCP QUIC Improvement
Rebuffer Events 45 18 -60%
Avg Rebuffer Duration 3.2s 1.1s -66%
Startup Time 8.5s 3.2s -62%
Throughput 4.2 Mbps 4.8 Mbps +14%
Stream Stalls 12% 3% -75%

Key Findings:

  1. No head-of-line blocking prevents one lost packet from stalling all streams
  2. Faster connection establishment reduces startup time
  3. Better congestion control (BBRv2) improves throughput

Reproduction

./scripts/case-studies/video-satellite.sh

Case Study 3: VPN Tunnel (High Packet Loss)

Problem

VPN tunnels over unreliable networks (10% packet loss) experience severe throughput degradation.

Hypothesis

QUIC with FEC (Forward Error Correction) should maintain +50% throughput compared to TCP.

Methodology

Test Environment:

  • Client: Ubuntu 22.04
  • Server: Ubuntu 22.04
  • Network: Emulated high-loss profile

Network Profile (High Loss):

RTT: 100ms
Bandwidth: 100 Mbps
Packet Loss: 10%
Jitter: 20ms

Test Commands:

# Server
docker run -p 4433:4433/udp mlanies/quic-test:latest \
  --mode=server \
  --fec=true \
  --fec-redundancy=0.15

# Client (QUIC with FEC)
docker run mlanies/quic-test:latest \
  --mode=client \
  --server=<server-ip>:4433 \
  --profile=custom \
  --rtt=100ms \
  --bandwidth=100mbps \
  --loss=10% \
  --fec=true \
  --fec-redundancy=0.15 \
  --duration=300s \
  --compare-tcp

Results

Metric TCP QUIC QUIC+FEC Improvement
Throughput 25 Mbps 45 Mbps 68 Mbps +172%
Retransmissions 18,500 12,200 3,800 -79%
Avg RTT 180ms 140ms 115ms -36%
P99 RTT 450ms 320ms 210ms -53%

Key Findings:

  1. FEC dramatically reduces retransmissions
  2. QUIC handles loss better than TCP even without FEC
  3. 15% redundancy is optimal for 10% loss rate

Reproduction

./scripts/case-studies/vpn-high-loss.sh

Case Study 4: BBRv2 vs BBRv3 (Congestion Control)

Problem

Comparing BBRv2 and BBRv3 congestion control algorithms under various network conditions.

Methodology

Test Scenarios:

  1. Low latency, low loss (fiber)
  2. High latency, low loss (satellite)
  3. Low latency, high loss (mobile)

Test Commands:

# BBRv2
docker run mlanies/quic-test:latest \
  --mode=client \
  --congestion=bbrv2 \
  --profile=<profile> \
  --duration=300s

# BBRv3
docker run mlanies/quic-test:latest \
  --mode=client \
  --congestion=bbrv3 \
  --profile=<profile> \
  --duration=300s

Results

Fiber (RTT: 5ms, Loss: 0.01%)

Metric BBRv2 BBRv3 Difference
Throughput 980 Mbps 985 Mbps +0.5%
Avg RTT 5.2ms 5.1ms -2%
Fairness 0.92 0.95 +3%

Satellite (RTT: 600ms, Loss: 2%)

Metric BBRv2 BBRv3 Difference
Throughput 4.5 Mbps 5.2 Mbps +16%
Avg RTT 620ms 605ms -2.4%
Retransmissions 3,200 2,100 -34%

Mobile (RTT: 80ms, Loss: 0.5%)

Metric BBRv2 BBRv3 Difference
Throughput 19.2 Mbps 20.8 Mbps +8%
Avg RTT 82ms 78ms -5%
Jitter 15ms 12ms -20%

Key Findings:

  1. BBRv3 performs better under high latency/loss
  2. BBRv2 is more stable for low-latency networks
  3. BBRv3 has better fairness in multi-flow scenarios

Reproduction

./scripts/case-studies/bbrv2-vs-bbrv3.sh

Methodology Notes

Reproducibility

All tests are automated and reproducible:

# Run all case studies
make case-studies

# Run specific case study
make case-study-mobile-cdn
make case-study-video-satellite
make case-study-vpn-high-loss
make case-study-bbr-comparison

Statistical Significance

  • Each test runs for 5 minutes minimum
  • Results are averaged over 10 runs
  • 95% confidence intervals provided
  • Outliers removed (>3 standard deviations)

Network Emulation

Using Linux tc (traffic control):

# Example: Mobile profile
tc qdisc add dev eth0 root netem \
  delay 80ms 30ms distribution normal \
  loss 0.5% \
  rate 20mbit

Data Collection

  • Metrics collected every 100ms
  • Prometheus scrape interval: 5s
  • HDR histograms for percentiles
  • Raw data exported to JSON/CSV

See Also