This podcast introduction was written by Nick Buraglio, the host of today’s podcast.
In the original days of this podcast, there were heavy, deep discussions about this new protocol called “OpenFlow”. Like many of our most creative innovations in the IT field, OpenFlow came from an academic research project that aimed to change the way that we as operators managed, configured, and even thought about networking fundamentals.
For the most part, this project did what it intended, but once the marketing machine realized the flexibility of the technology and its potential to completely change the way we think about vendors, networks, provisioning, and management of networking, they were off to the races.
We all know what happened next.
Michael Mullany analyzed 20 years of Gartner hype cycles and got some (expected but still interesting) conclusions including:
- Nobody noticed major technologies even when they were becoming mainstream
- Lots of technologies just die, others make progress when nobody is looking
- We might get the idea right and fail badly at implementation
- It takes a lot longer to solve some problems than anyone expected
Enjoy the reading, and keep these lessons in mind the next time you’ll be sitting in a software-defined, intent-based or machine-learning $vendor presentation.
Loved the article from Philip Laplante about environmental antipatterns. I’ve seen plenty of founderitis and shoeless children in my life, but it was worshipping the golden calf that made me LOL:
In any environment where there is poor vision or leadership, it is often convenient to lay one’s hopes on a technology or a methodology about which little is known, thereby providing a hope for some miracle. Since no one really understands the technology, methodology, or practice, it is difficult to dismiss. This is an environmental antipattern because it is based on a collective suspension of disbelief and greed, which couldn’t be sustained by one or a few individuals embracing the ridiculous.
That paragraph totally describes the belief in the magical powers of long-distance vMotion, SDN (I published a whole book debunking its magical powers), building networks like Google does it, intent-based whatever, machine learning…
A while ago we discussed a software-focused view of Network Interface Cards (NICs) with Luke Gorrie, and a hardware-focused view of them with Or Gerlitz (Mellanox), Andy Gospodarek (Broadcom) and Jiri Pirko (Mellanox).
Why would anyone want to implement features in hardware and not in software, and what would be the best hardware implementation? We discussed these dilemmas with Silvano Gai in Episode 110 of Software Gone Wild podcast.
Over the last weekend I almost got pulled into yet-another CLI-or-automation Twitter spat. The really sad part: I thought we were past that point. After all, I’ve been ranting about that topic for almost seven years… and yet I’m still hearing the same arguments I did in those days.
Just for the giggles I collected a few old blog posts on the topic (not that anyone evangelizing their opinions on Twitter would ever take the time to read them ;).
AI is the new SDN, and we’re constantly bombarded with networking vendor announcements promising AI-induced nirvana, from reinventing Clippy to automatic anomaly- and threat identifications.
If you still think these claims are realistic, it’s time you start reading what people involved in AI/ML have to say about hype in their field. I posted a few links in the past, and the Packet Pushers Human Infrastructure magazine delivered another goodie into my Inbox.
The last Software Gone Wild podcast recorded in 2019 focused on advances in Linux networking - in particular on interesting stuff presented at NetDev 0x13 conference in Prague. The guests (in alphabetical first name order) Jamal Hadi Salim, Shrijeet Mukherjee, Sowmini Varadhan, and Tom Herbert shared their favorite topics, and commented on the future of Linux networking.
I stumbled upon a great MIT Technology Review article (warning: regwall ahead) with a checklist you SHOULD use whenever considering a machine-learning-based product.
While the article focuses on machine learning at least some of the steps in that list apply to any new product that claims to use a brand new technology in a particular problem domain like overlay virtual networking with blockchain:
Someone sent me this observation after reading my You Cannot Have Public Cloud without Networking blog post:
As much as I sympathize with your view, scales matter. And if you make ATMs that deal with all the massive client population, the number of bank tellers needed will go down. A lot.
Based on what I read a while ago a really interesting thing happened in financial industry: while the number of tellers went down, number of front-end bank employees did not go down nearly as dramatically, they just turned into “consultants”.
One of my subscribers was interested in trying out whitebox solutions. He wrote:
What open source/whitebox software/hardware should I look at if I wanted to build a leaf-and-spine VXLAN/EVPN/BGP data center.
I don’t think you can get a fully-open-source solution because the ASIC manufacturers hide their SDK behind a mountain of NDAs (that strategy must make perfect sense – after all, it generated such awesome PR for NVIDIA). Anyway, the closest you can get (AFAIK) if you're a mere mortal is Cumulus Linux, and you just choose any whitebox hardware off their Hardware Compatibility List.
I got interesting feedback from one of my readers after publishing my REST API Is Not Transactional blog post:
One would think a transactional REST interface wouldn’t be too difficult to implement. Using HTTP1/1, it is possible to multiplex several REST calls into one connection to a specific server. The first call then is a request for start a transaction, returning a transaction ID, to be used in subsequent calls. Since we’re not primarily interested in the massive scalability of stateless REST calls, all the REST calls will be handled by the same frontend. Obviously the last call would be a commit.
I wouldn’t count on HTTP pipelining to keep all requests in one HTTP session (mixing too many layers in a stack never ends well) but we wouldn’t need it anyway the moment we’d have a transaction ID which would be identical to session ID (or session cookie) traditional web apps use.
A long while ago Daniel Dib wrote a nice blog post on “SDN will make the networking engineers obsolete” theme. While it sounds like beating a dead horse, the SDN craze isn’t subsiding, so another healthy dose of common sense might come handy.
Hint: if you’re not following Daniel’s blog, you should… even though he decided to make old farts’ life harder by publishing on LinkedIn.
Sick-and-tired of intent-based GUIs that are barely better than CiscoWorks on steroids? How about asking Siri-like assistant queries about network state in somewhat-limited English and getting replies back in full-blown sentences?
Someone working for a network automation startup desperately tried to persuade me how cool their product is. Here’s what he sent me:
We let network engineers build their own network automation solutions in no time without requiring coding or scripting knowledge. It’s all GUI based, specifically geared towards network engineers - they can simply model services or roll-out networks “as-designed”.
The only problem: I’ve seen that same argument numerous times…
This blog post was initially sent to subscribers of my SDN and Network Automation mailing list. Subscribe here.
Have you ever seen a presentation in which a startup is telling you how awesome their product is because it allows you to simulate your whole network in a virtual environment? Not only that, you can use that capability to build a test suite and a full-blown CI/CD pipeline and test whether your network works every time you make a change to any one box in the network.
Sounds awesome, right? It’s also dead wrong. Let me explain why that’s the case.