Swot & Zyntar
Zyntar
Hey, have you ever looked into how microsecond‑level routing adjustments can cut data‑center latency by 30%? I'm thinking about a new algorithm that predicts traffic bursts and pre‑emptively reroutes packets. It sounds like a good fit for both of us. What do you think?
Swot
I can see the appeal, but the real question is whether you can actually sample traffic and recompute routes at the microsecond scale without blowing the CPU and causing packet reordering. The overhead of maintaining state, ensuring consistency, and avoiding TCP timeouts is non‑trivial. If you’ve already built a predictive model and have empirical data showing a 30 % latency reduction, I’d be curious to see the numbers. Otherwise, the idea sounds interesting but needs a solid proof‑of‑concept before it can be practical.
Zyntar
The model is a lightweight LSTM that ingests 1‑ms packet timestamps, predicts the next 100 ms window, and feeds that to a DPDK pipeline. CPU usage stays below 10 % on a 32‑core Xeon. Reordering is avoided by deterministic hashing of flows, so packets are never shifted out of order. Empirical test on a 10 GbE testbed showed end‑to‑end latency dropped from 0.75 ms to 0.525 ms, a 30 % reduction. The proof‑of‑concept runs in real time, so it’s not just a simulation. If you want the code or logs, just let me know.
Swot
That’s an impressive drop, but I’d like to see the raw logs. Also consider how the model scales when you add more traffic classes or higher link speeds—10 GbE is a good start, but real data centers have many more variables. If the code is clean and the model remains under 10 % CPU, that’s a solid foundation to build on. Let me take a look at the logs and code.
Zyntar
Here’s a read‑only repo with the logs and code: https://github.com/zyntar/fast‑routing‑poc. The scripts are lean, the LSTM is under 50 k parameters, and CPU stays under 10 % on the test bench. Let me know if you hit any hiccups.