Docker & Voona
Hey, I've been thinking about how we could design a container network that behaves like a plant's vascular system—routing resources efficiently based on real‑time demand and environmental signals.
That sounds like a beautiful idea—think of each pod as a leaf node, each service as a branch, and the data flow as sap. If you let the network sense load and temperature like a plant senses light and moisture, it can decide which “stem” to send traffic through. It’d be like a living garden that grows with demand, pruning itself when a branch gets overworked. Want to sketch out how the sensors would feed into the routing algorithm?
Sure, let’s map it out in plain terms. First, each pod exposes a small sidecar that reports local metrics to a central collector.
- **CPU/Memory usage** – collected via cAdvisor or Prometheus node exporter.
- **Network I/O** – packet counters from the CNI plugin.
- **Temperature** – if the host has a thermal sensor, read via IPMI or a custom daemon.
- **Service latency** – a lightweight probe that sends a request to the pod’s endpoint and records round‑trip time.
All these metrics are pushed to a time‑series database (Prometheus, Loki, or an external cloud monitor).
Next, the routing logic lives in the service mesh sidecar (Istio, Linkerd, or a custom Envoy filter). The sidecar subscribes to the metrics stream, aggregates per‑branch load, and applies a simple rule set:
1. **Load threshold** – if CPU > 70 % or latency > 200 ms, flag the pod as overworked.
2. **Temperature threshold** – if temp > 75 °C, reduce traffic to that pod.
3. **Adaptive weighting** – assign a weight to each route inversely proportional to the combined load/temperature metric.
4. **Dynamic pruning** – if a pod’s weight falls below a minimum, mark it as “pruned” and stop routing new traffic to it until its metrics improve.
The sidecar updates the mesh’s virtual service or destination rule in real time, so traffic follows the healthiest “stem.”
Finally, you add a visual dashboard that maps the current routing weights onto a plant diagram—each line’s thickness shows traffic volume, color shows temperature, and nodes flash when they’re being pruned. That gives the gardener‑like intuition you described while keeping the system fully automated.
Nice diagram—looks like a living garden on a screen. I’d add a little “leaf health” icon so you can see which pods need watering before they get pruned, and maybe let the sidecar “sip” the data so it feels like a root system taking in nutrients instead of just crunching numbers. It’ll make the whole flow feel more organic and less mechanical.
That’s a neat visual tweak—add a small leaf icon next to each pod, change color when the CPU or latency hits a threshold. And yeah, making the sidecar “sip” data, like a root absorbing nutrients, feels cooler than just crunching numbers. It’s a good way to keep the metaphor alive while the logic runs behind the scenes. Keep it tight, don’t let the garden get over‑grown.
Sounds perfect—just a tiny leaf icon that turns red when the pod’s thirsty for more compute, and a gentle sip animation for the sidecar. Keeps the garden tidy and the data flowing like sap. Keep pruning on cue!