I'm getting back in to audio programming, starting off with Pd[1] and reading Miller Puckette's book[2]. I'm planning on writing some low-level C libraries afterwards, using The Audio Programming Book[3] as a guide
What's your view on how these people actually impacted the adoption of SDN in general?
> The investments NSF made in SDN over the past two decades have paid huge dividends.
In my view this seems a little overblown. The general idea of separation of control and data plane is just that - an idea. In practice, none of the early firms (like Nicira) have had any significant impact on what's happening in industry. Happy to be corrected if that's not accurate!
Depends where you are in the industry - the hyperscalers specifically have budget to afford a team to write P4 or other SDN code to manage their networks in production, so they're probably the biggest beneficiaries.
Lower end, it did make programmability more accessible to more folks and enabled whitebox switches to compete against entrenched players to a far greater extent than previously possible. Again, hyperscalers are going to be the main folks who buy this kind of gear and run SONiC or similar on it, so they can own the full switch software stack.
Many of the startup companies in the SDN space did have successful exits into larger players - for example Nicira into VMWare, Barefoot (Tofino switch chip) and Ananki (the ONF 4G/5G spinoff) into Intel. Also, much of the software was developed as open source, and is still out there to be used and built on.
Scrolling is broken on mobile, it scrolls right past entire sections, like there's too much inertia. The last section is also wider than the screen and horizontal scrolling disabled.
> On the other hand, it’s missing what I think is a major problem. Much of academic systems work already seems bottlenecked on selecting which problems to pursue. This appears to be two reasons. The short-term reason is disconnection from the problems that the customers of systems (in industry and the wider world) face. The longer-term, and more important, reason is that coming up with a vision for the future is just much harder than hill climbing. It takes more experience, more insight, and more vision to choose problems than to optimize on them. It takes more taste to reject noise, and avoid following dead ends, than to follow the trend.
While this is certainly inarguably true, I think the whole point is that AI-Driven Research for Systems as the authors put it, makes this much less critical. The sheer volume of problems we'll be able to solve will drastically minimise selection paralysis.
reply