I’ll share some examples in a series og blog posts, first up one on radio noise.
This happened one early Monday morning late November. Our cloud diagnostics algorithm suddenly started screaming. Our Oulu office router was logging noise data that almost went through the roof. What the heck was going on?
I started diving into the data and found radio noise levels on the 5GHz band up towards -50 db. This is 40 db over the noise floor, or approx 10,000 times the normal noise level. Noise at these levels basically kill the network.
Interesting radio noise |
We jumped on call with Oulu and learned that Jukka, our new Linux wizard, had received his new Lenovo laptop that morning. And the router was placed on top of the laptop. So fair assumption was that the laptop was the culprit, but why the spikes?
Some more testing and many creative theories later we nailed it. It was the power supply. But the catch was, it was only polluting when the laptop battery was charging! Good luck figuring that out without reliable diagnostics.
Router on laptop with power supply nearby |
In the next post we’ll share some super interesting learnings on latency and buffer-bloat. Stay tuned!
If you would like someone to review a preprint of your upcoming bufferbloat piece feel free to contact me or those on the bloat mailing list.
ReplyDeleteA good tool to look at bufferbloat related problems is the netperf-wrapper rrul test suite.
If your device is linux derived, the sqm-scripts (with fq_codel) are capable of tuning up your isp up and downlink to have maximum throughput with minimum latency. The results of a typical tuning session for a cable modem:
http://burntchrome.blogspot.com/2014_05_01_archive.html
If you want to see the horribleness that most 802.11ac drivers have in them, try the netperf-wrapper rtt_fair4be test to 2 or more stations at the same time.
Thanks Dave, appreciate that. Will IM you. We have applied traffic shaping based partly on inspiration from you guys at Bufferbloat project, and in many cases see the typical result. We do however see significant variations that we guess stem from upstream network topology, so we try to learn and dynamically optimize for each individual router.
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