Almost 8 years ago, when I was working as a Monitoring SRE at Google, I wrote a proposal to use compressed sensing to reduce storage and transmission costs from linear to logarithmic. (The proposal is also available publicly, as a defensive publication, after lawyers complicated it beyond recognition https://www.tdcommons.org/dpubs_series/954/)

I believe it should be possible now, with AI, to train online tiny models of how systems behave in production and then ship those those models to the edge to use to compress wide-event and metrics data. Capturing higher-level behavior can also be very powerful for anomaly and outlier detection.

For systems that can afford the compute cost (I/O or network bound), this approach may be useful.

This approach should work particularly well for mobile observability.