QUIC CPU Optimization Lab

CPU load reduction in QUIC tunnels: eBPF/XDP, io_uring, SIMD crypto acceleration

Closed Lab Started: December 2025

Project Overview

QUIC CPU Optimization Lab is a research project aimed at reducing CPU load in QUIC tunnels (MASQUE/CONNECT-IP) for the CloudBridge platform. The project explores kernel-level optimization methods (eBPF/XDP), zero-copy I/O (io_uring), and SIMD crypto acceleration.

The project is closed due to the commercial value of developments and the need to protect unique findings. Access is provided through sponsorship and partnership.

Key Objectives

20-60%

CPU load reduction

30-40%

Throughput increase

RFC

Full compatibility

Open

Results publication

Research Areas

eBPF/XDP

Offload packet classification to kernel space, reduce syscall overhead, XDP-based routing decisions.

  • Code skeleton ready
  • Loader implemented

Zero-copy I/O

io_uring to eliminate copying between kernel and userspace, async I/O for TUN/TAP.

  • In active development

SIMD Crypto

Parallel packet encryption AVX2/AVX-512, batch encryption, hardware AES-NI.

  • Planned

Adaptive Batching

Small packet aggregation, per-packet overhead amortization, dynamic batch size adaptation.

  • Planned

Congestion Control

Tunnel-optimized algorithms, encapsulation awareness, VPN workload optimization.

  • Planned

Profiling

CPU/Memory profiling with pprof, baseline benchmarks, comparative analysis.

  • Infrastructure ready

Architecture 

┌─────────────────────────────────────────────────────────┐
│        QUIC CPU Optimization Lab (Go)                   │
│                                                         │
│  ┌──────────────────────────────────────────────────┐   │
│  │  Baseline Benchmarks                             │   │
│  │  • Throughput measurement                        │   │
│  │  • Latency profiling                             │   │
│  │  • CPU/Memory profiling (pprof)                  │   │
│  │  • Packet rate testing                           │   │
│  └──────────────────────────────────────────────────┘   │
│                          │                              │
│                          ▼                              │
│  ┌──────────────────────────────────────────────────┐   │
│  │  Optimization Implementations                    │   │
│  │  • eBPF/XDP packet classifier (C)                │   │
│  │  • io_uring zero-copy I/O                        │   │
│  │  • SIMD crypto acceleration                      │   │
│  │  • Adaptive batching                             │   │
│  └──────────────────────────────────────────────────┘   │
│                          │                              │
│                          ▼                              │
│  ┌──────────────────────────────────────────────────┐   │
│  │  Comparative Analysis                            │   │
│  │  • Baseline vs Optimizations                     │   │
│  │  • Performance metrics                           │   │
│  │  • Statistical validation                        │   │
│  └──────────────────────────────────────────────────┘   │
│                          │                              │
│                          │ Integration                  │
│                          ▼                              │
│  ┌──────────────────────────────────────────────────┐   │
│  │  CloudBridge MASQUE VPN                          │   │
│  │  • Production deployment                         │   │
│  │  • Real-world validation                         │   │
│  └──────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────┘

QUIC CPU Optimization Lab research infrastructure architecture

Baseline Measurements (macOS M1)

Throughput

0.38-0.97 Gbps

depending on packet size

Latency

~43 μs

average

Packet Rate

7K-46K pps

packets per second

CPU Profiling

pprof

baseline_cpu.prof available

Current Status

Implemented & Tested

  • Baseline benchmarks (throughput, latency, CPU, packet rate)
  • CPU/Memory profiling with pprof
  • Automation via Makefile (30+ commands)
  • Docker environment for reproducibility
  • eBPF/XDP code skeleton and loader

In Active Development

  • eBPF/XDP packet classifier for kernel-space processing
  • io_uring integration for zero-copy I/O
  • Detailed baseline results analysis
  • Comparative optimization testing

Planned

  • SIMD crypto acceleration (AVX2/AVX-512)
  • Adaptive batching and packet coalescing
  • Tunnel-optimized congestion control
  • Scientific paper publication with results
  • University lab work (Q1 2026)

Technical Details

Technologies

Go 1.22+ C (eBPF) io_uring XDP AVX2/AVX-512 AES-NI pprof Docker

Standards

  • • RFC 9000 (QUIC)
  • • RFC 9298 (MASQUE)
  • • RFC 9484 (CONNECT-IP)

Platforms

  • • Linux (полное тестирование)
  • • macOS (разработка)
  • • Docker (воспроизводимость)

Tools

  • • Makefile (30+ команд)
  • • pprof (CPU/Memory)
  • • Custom benchmarks

Closed Laboratory

Partnership Access

Access:

  • CloudBridge Team
  • Trusted Researchers
  • Corporate Partners

Why Closed?

  • Unique developments
  • Commercial value
  • Technological security
Become Partner

Publications

Open Results

Research results will be published as scientific papers and reproducible benchmark data.

  • Scientific paper (planned)
  • Benchmark methodology
  • Lab work (Q1 2026)

Related Projects

Related Labs

Technologies

Contact

For partnership inquiries and access to the closed laboratory:

info@cloudbridge-research.ru Become Partner