Performance Architecture & Zero-Transcoding Advantage
RUSEON Core is engineered from first principles in Go to deliver predictable, linear performance scalability across edge gateways, virtualized environments, and high-density bare-metal servers.
1. Zero-Transcoding CPU Efficiency
Traditional video management systems transcode every incoming stream into web-friendly formats by decoding 1080p/4K compressed frames into raw YUV pixels and re-encoding them. This burns massive CPU and GPU resources.
RUSEON Core replaces transcoding with Zero-Transcoding Transmuxing:
- Video frames (H.264 / H.265 NAL units) are accepted directly from RTSP and repackaged into destination containers (RTP packets, fMP4 fragments, MPEG-TS chunks) in memory without decoding.
- Reduces CPU utilization by over 90%, eliminating the need for dedicated GPUs.
- Delivers video frames to WebRTC viewers with microsecond transit latency.
2. Core Performance Pillars
| Pillar | Technical Implementation | Real-World Benefit |
|---|---|---|
| Shared-Memory Broadcast | Ring Buffer with atomic pointers | 1 in-memory copy serves WebRTC, HLS, Archiver, and AI |
| Adaptive UDP Batching | Linux sendmmsg in WebRTC Pipeline | 85–90% kernel syscall reduction during live streaming |
| Direct I/O Cache Eviction | sync_file_range + POSIX_FADV_DONTNEED in Recorder | 0% Page Cache thrashing, flat memory usage during 24/7 writes |
| Sub-Millisecond Playlists | In-memory HLS muxer with singleflight concurrency | Serves HLS playlists in < 0.5ms with 0 disk access |
3. Performance Guide Directory
- Empirical Benchmarks: Official load test results with 600 cameras, 1,800 HLS viewers, and 240 WebRTC peers.
- Capacity Planning Guide: Sizing CPU, RAM, Network, and Storage for your specific camera fleet.
- Horizontal & Vertical Scaling: Scaling RUSEON Core across NUMA nodes, edge clusters, and load balancers.