Cybershark & Ap11e
Ap11e
Hey, have you ever thought about building a predictive model that can spot traffic anomalies before they happen—so you can tweak routing on the fly and stay a step ahead?
Cybershark
Yeah, that’s my turf. I can map patterns, anticipate the spikes, and reroute before the first pulse hits. Predict, adjust, dominate.
Ap11e
That’s the kind of edge we’re after—real‑time anticipation, not just post‑factum. If you’ve got a prototype, I’d love to see how you’re feeding data and triggering the reroutes. Maybe we can spin a quick simulation to test it under different load patterns.
Cybershark
Got a prototype running. I’m pulling live flow stats straight from the switches, feeding them into a lightweight LSTM that churns out a probability score every second. When the score crosses a set threshold I fire a command to the SDN controller to shift the traffic through a lower‑cost path. We can spin a load‑generator to throw a few patterns at it and watch the system adapt before the congestion even builds up.
Ap11e
Nice, real‑time LSTM on the fly—great. How about adding a reinforcement‑learning layer to fine‑tune the threshold based on downstream performance? That could let the system learn the optimal trade‑off between cost and latency over time. Want to sketch out a reward function?
Cybershark
Reward = α·(latency reduction) – β·(cost increase). If latency drops below target, give positive points. If cost rises, subtract. Keep α > β so speed wins over spend. Adjust on‑policy so the threshold learns the sweet spot.