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Arterialist 2 days ago [-]
Hey guys, I had a few esp32 boards around for a while and always thought - how do we go beyond just one device, would it be possible to apply cloud-native thinking to distributed edge computing?
Result is Kube32. I got the cluster and a full-stack web app with real-time sync running on esp32 s3 dev board and cardputer - https://7f0c-180-74-71-21.ngrok-free.app/ (may go down) (should support at least a few concurrent users - break it)
You won't see one cool thing though, which is the hud pod that uses Cardputer as a keyboard that syncs keystrokes to all clients in real-time.
Honestly, it opens so many possibilities for distributed edge computing and multi-device systems. Let me know what use-cases do you think would benefit from kube32 the most.
ZenoArrow 4 hours ago [-]
Thank you for working on this. The most impressive part for me is getting Kubernetes running on such low spec hardware. I can't think of a use case I have for Kube32 at the moment, but I do have use cases for Kubernetes running on a small amount of RAM. Is there any chance of porting this back to x86/x86-64 PCs or ARM SBCs? For example, being able to run this on a cluster of Raspberry Pis would be interesting as it would free up more RAM for applications.
NewJazz 3 hours ago [-]
I feel like a gradualist approach may be better for those applications. Start with "vanilla" k8s, and iteratively target subsystems to shrink down. Either by porting to Rust or simply optimizing the Go code for low RAM environments.
Arterialist 3 hours ago [-]
Hmm, have you tried k3s? It promises low ram, but it could still be too much, I'm not sure about your requirements. In the meanwhile I'll see if it can be pushed further, but I guess we both know the answer :)
ahmeni 2 hours ago [-]
k3s does well on low ram but I've found it still uses a chunk of CPU.
gawacer 2 hours ago [-]
[dead]
catlifeonmars 4 hours ago [-]
What kind of overhead does the Wasm approach add in terms of memory and cpu time? I also have way too many ESP32 boards lying around
Arterialist 3 hours ago [-]
I'll measure and let you know later, would appreciate if can do measurements on your hardware too as I only have a few options to test on
vrighter 4 hours ago [-]
cloud native emedded devices are an oxymoron
wormik 1 hours ago [-]
[dead]
rayshen 6 hours ago [-]
What's the benifit?
Arterialist 3 hours ago [-]
there are few imo:
- trustless code execution on someone's hardware (kube32 supports gpio permissions, amongst other stuff)
- your standard Kubernetes cluster can now reach the physical world natively
- OTA updates at arbitrary scale
- fault-tolerance in the physical world, orchestration of workloads based on physical proximity or other sensor data
Result is Kube32. I got the cluster and a full-stack web app with real-time sync running on esp32 s3 dev board and cardputer - https://7f0c-180-74-71-21.ngrok-free.app/ (may go down) (should support at least a few concurrent users - break it)
You won't see one cool thing though, which is the hud pod that uses Cardputer as a keyboard that syncs keystrokes to all clients in real-time.
Honestly, it opens so many possibilities for distributed edge computing and multi-device systems. Let me know what use-cases do you think would benefit from kube32 the most.
- trustless code execution on someone's hardware (kube32 supports gpio permissions, amongst other stuff) - your standard Kubernetes cluster can now reach the physical world natively - OTA updates at arbitrary scale - fault-tolerance in the physical world, orchestration of workloads based on physical proximity or other sensor data
those are just a few that come to mind