Are generative AI and LLMs the future of SDN?

Hey, do you remember SDN? No, I'm being serious, software defined networking. That was pretty much every other blog post and podcast five years ago, And I can't remember the last time that I heard someone talk about it since then. So what happened? I mean, is SDN still a thing? Was it just so successful that we don't need to talk about it anymore? Yeah, I know I'm being ridiculous, but seriously, what's going on with SDN these days?

Joining me today is my new friend, Leon Adado, fellow technical evangelist with Mihira Kentech, a technologist with years and years of experience in networking and specifically network visibility. And today, that's what we're gonna talk about kind of. I mean, we don't really know the exact answer of what the deal is with SDN. But we have some ideas.

My name is Philip Jervasi, and this is telemetry now.

Hey, Leon. Welcome. It's really great to have you, and welcome to Kentech. It's been a few weeks now, I think. Right?

Is that That it's it's it's been a few weeks, but I I say that I've been here for about twelve minutes, maybe thirteen at this point.

But yeah, it is amazing to be here. I've, you know, known a lot of folks at Kentic for a while and sort of been pining to be part of the team and they finally let me sit with the cool kids. In the lunch at the lunch table.

With the cool kids at the lunch table, which brings back to my memory all sorts of like eighties movies of teenage angst and all that that fun stuff.

So Yeah.

Yeah. As as is want. Right? And I mean, like, isn't that the the mode of the days is going back to the eighties?

It's all the all the movies and everything, like, It really is.

We watched, our family movie night the other night was, ghostbusters. My son didn't watch it. He's only nine, and so he was in bed. But my daughters are are older.

So we watched Ghost Busters. That was a lot of fun. My my wife loves Molly ringwell movies. We love eighties music.

We listen to a lot of journey in Peter Gayle Peter Gabriel in my house. I like other stuff too. I'm gonna be wrong. I'm a product of the nineties more of the early nineties.

I was in the throes of high school when, Nirvana washed up from the shores of Puget Sound and and took over the world. So that was a lot of fun.

Wow. So I'm about ten years ahead or behind or however you wanna do time wiggly wobbly timey whiny stuff. But, yeah, the eighties are squid the way the eighties were about me.

Like, all those movies that you named were happening while I was in it.

Yeah. Loved it. I love it. I love it very much. I don't know what's going on today.

You know what? I sound like such an old curmudgeon as the words are coming out of my mouth. I don't know what's going on today with these kids, but I watch my daughter and my son to a lesser extent. But and I look at them and I'm like, You know, they're my my oldest daughter who's almost seventeen, she's starting to wear doc Martins again.

She loves us meshing pumpkins. She wears flannels now. And my my middle daughter are a little bit less so, but, you know, similar. And I'm looking at them, like, you guys think I'm like an old, old guy, but you're, you know, we my generation invented the stuff that apparently you think is the coolest stuff out there.

So you're I will tell you that when I was, you know, younger, I was preteen, Happy days was all the rate in the seventies.

Happy days was all the rage. And I heard the same thing, you know, my parents and their friends were like, You think you invented all of this stuff? You know, it was all coming back. Everybody wanted to be fonzy and, you know, so I think it's just cyclical. I think that that Vintage is, you know, just, you know, twenty five years behind whatever you are as a teenager.

I agree. In fact, I'll share this another time when we have maybe a podcast episode dedicated specifically on music trends, but I have a theory that sort of breaks down now in the age of the internet, but I have a theory about popular music. I wanna talk about it with you one day.

I'm gonna leave it as a cliffhanger for our audience.

One day. I'm gonna do a podcast about it. Yeah. Right. I did share it internally with our team and, a couple people, they they were very quick to find all the holes in my logic. So that made me feel bad.

Which which makes it more fun?

Yeah. That's true. So you are a technical evangelist with Kentic as I might. But is that what you have been doing for your career? Or, has there been a journey in a progression to what you're doing?

There's always there's always a journey. And and I am a man of a particular age. I mean, I've I've basically dated myself at this point. I'm I'm at the point in my career where I find that my goal is to use all the privilege that I have and open doors and shove other people through it.

And, you know, when I'm when I'm talking to folks, I remind them that that everything that we're doing is sort of a journey. There's no, there's no, oh, and I was born that way or This is I learned this and that's the way I stayed for the rest of my career. None of those things are ever true. So I actually, my going all the way back, my degree is in theater.

Because in nineteen eighty five when I started college, the internet was certainly not a thing and computers were barely a thing. And when I got out of college to work in tech, the two things you needed were breathing in a suit and one was actually optional. So I got into, you know, computers when you could get windows for free on five, on twelve five and a quarter inch floppies. And you got it for free because it came it came with Excel one point o, which nobody ever used because everyone was still using Lotus one two three.

So that was where I started. And I actually started doing training because effectively, you know, computer training is nothing but stand up. It's just a little bit more specific than a stand up routine.

And it fed that theater urge in my soul.

And I worked my way up the IT food chain from training to desktop support to, SIS admin and, you know, sort of step sideways into network engineering And then, I got involved about twenty five years ago with monitoring automation and systems management, which was fairly new at the time.

And I've never looked back. I've got I've been building, maintaining, tweaking monitoring systems using all kinds of software everything from Tivoli and Open View and Yankee Pearl scripts, which is really Tivoli.

All over again. And, you know, all the way up through, you know, grafana and Zavix and Janky Python scripts and, you know, stuff like that. So, and, and of course, can't take. So that's that's my journey in sort of a very tight nutshell. It's my my IT superhero origin story. I was dropped in a vat of floppy disks as a baby.

So, when you said theater, of course, what immediately came to mind was John Lovett's when he's like, acting. Yeah.

And, that's, yeah, I don't know if that's accurate or not, but, that's what came to my mind. So here we are. And, today, I spoke to Christoph Fisher. He's the chief product officer at Kenton. We we we we did a we did a LinkedIn live.

And, and it's already out. So if you're listening to this podcast right now, you can still go back and listen to that or watch that because that is video. And, and check out what he has to say because what he was talking about and what we talked about together, it was specifically the role of generative AI generative, not general, generative AI, and large language models in IT operations. Now we did focus a little bit more on networking than anything else because we are a networking focused company, but IT operations in general as well.

Yeah. But it got me thinking, and then I brought it up to you. You know, this really feels it smells. It has this flavor of what I always thought SDN software defined networking was always supposed to be.

And then I started thinking, we don't talk about SDN anymore. That was ten years ago. There's a few years where every single blog, every single video, every single podcast we've done SDN, and then and done. You know, there was all this like vaporware stuff and architecture and, you know, thought leerish kind of stuff, thought literary kind of stuff, and then then we stopped hearing about it.

It's always been in my mind. And, you know, we we have these manifestations of SDN or s SDN SDN actually. That was a Freudian slip because SDN, I think, is an actual manifestation of SDN that we can point to. Anyway, I I really feel like this is kind of now coming into fruition where we're we're we're talking about this software overlay to physical and virtual infrastructure and configuration, all these things.

Where human beings are interfacing with it in a very different way.

And it's really exciting to me. But anyway, that's what we were talking about. I wanted take on it because I know you have strong opinions about, well, everything. But in this case, specifically, strong opinions about, the the the architecture and smoke and mirrors surrounding GenAI and large language models.

So I yeah. I I think that SDN, the concept.

The movie, the book, the sequel.

I think the concept of SDN was really exciting for a lot of us. As much as it never really came to fruition. But even things as simple as having a tool that would configure your network infrastructure, like pre provision your network infrastructure. And then when it saw where it was, it saw its IP address or it saw the VLAN it was part of would automatically start to put more configurations into play automatically.

And you know, being able to respond to the circumstances on the network and update the the configuration based on that. Maybe we're gonna reroute traffic this way or this kind of traffic this way or whatever it is. That was the promise of SDN and it very quickly you know, proved itself to be way more complicated than anybody wanted it to be. And I think that's one of the reasons why people backed away from it, but the promise of it and we could see the seeds of it because we have tools that will pre provision, you know, that will keep a whole bunch of code snippets and just throw them on boxes.

And we have things that will respond to a monitoring event. You know, oh, we saw, you know, traffic spiking of this we saw this kind of flow going in this direction and, you know, let me throw a different configuration on there. We we can imagine it, but it doesn't quite go there.

So yeah, I I I think that the idea of SDN was really brilliant and, in in the talk with with Kristoff, you even said, I'm I'm gonna steal your movie analogy. You were, you know, you quoted, Star Trek and you you quoted the point where Scotty was sitting down with a modern day, which was, I think, an Apple two CI in the movie or something like that, just this, you know, beige box kind of thing. And he goes to Yeah. You know, he goes to talk to the computer, and it doesn't respond.

And the person picks up a mouse and says you have to use this. And so he rolls the mouse over looking at the the the roller ball and says computer. And you know, we all in the audience busted up because that's not how that works. But, you know, you had said during your talk with Kristoff that, you know, that's how you felt the computers ought to interact and SDN was an aspect of that, you know, determine, look at my current circumstances and, you know, help me come to a resolution, help me get to a finished product of some kind.

And that's what people are seeing with generative AI now. I mean, chat Jippity and yes, I insist on calling it chat Jippity.

Thank you Corey Quinn.

Really that's that's its promise is that I can give it a couple of sentences. I want you to do blah blah blah blah blah blah blah and it will make a damn good attempt at trying to do that. So I I think that's where we start.

Right?

Is is I think SDN software defined networking was just so ambiguous that it meant very little.

And so, therefore, it was just this nebulous thing that we couldn't nail down. Is this SDN or is this SDN? What about this product? And so it just remained in the realm of smoke and mirrors and and Mark texture and vaporware, all that stuff.

Because it was it was that nebulous ambiguous thing that nobody could pin down Nevertheless, this idea of software being a, you know, adding abstraction layers between human beings and the the underlying network and infrastructure. Well, that's kind of always been there. Right? Isn't a VLAN and a software defined element, you know, I mean, it's a logical element, not a physical box thing that we can touch and and, you know.

And so, and so I think that it started to die. It started to die down as a term that was popular to use. Now, I've always found that, like, the kinda like the thought leader circles would be talking about things that were maybe three, five, even seven years ahead of where the industry was. And so maybe this one was just a little delayed.

And so ten years that we're talking about SDN, and now it's finally coming to wishing. So maybe that's what it is. But there were an r, I think, very palpable manifestations of these software extraction layers in between a human and and underlying hardware like SD WAN and like other, control plane platforms whether they be homegrown kind of things or some, you know, some vendor creating another, control plane mechanism to manage an entire data center or whatever it happens to be. And then, of course, we had network automation become very, very ubiquitous where, at one point, it was just relegated to, like, hipster now.

Everybody's kinda doing it in a sense that you can, you know, ingest all this information into your iPad and into your CRM and all these things. And then call on those in your Python scripts and answerable playbooks to do certain things, to pre provision devices, to elicit a, a, some kind of, configuration push when some kind of event is so we can do that stuff I just think it's become less cool to talk about because we're starting to see those manifestations. And and I think that what we're seeing now with, the application of generative generative AI, large language models in the realm of IT operations is another manifestation of that and a very, very one.

And thank you for bringing up that Star Trek team. One of my favorites. I love it. I've made a couple of memes of that one in particular, but I will also allude to Star Trek the next generation because I've always wanted to well, not as much anymore because I'm not a network operator.

But when I was, I always wanted to be able to talk to the network, like Jordy talked to Excuse me, lieutenant commander, Jordan Yes. Forage, give her where it's due.

You know, I've wanted to be able to talk to the network in that natural way, he spoke to the enterprise, to the enterprise computer. And so that's what CP talked a lot about today. He talked about the natural language query, which is based on the concept of natural language processing, which is kind of the umbrella over large language models. And it's basically providing an interface between, you know, you and me as human beings, and data. Yeah.

Not not we can expand data, but actual data is also an AI.

And then being able to query it, which is where we get NLQ, you know, ultimately leading toward other other cool things like automated remediation and automated root cause analysis.

And that's Well, and I So I I I I think that I think what you've just done is interpreted the the current technology that again LLM and generative AI through the lens of the work that you do.

And I think that lots of people I think lots of people are doing that. What what's interesting is that you are finding this l l m, again, chat Jippity bolt on, you're finding it in Salesforce. You're finding it being built into Octa. You're finding it being built into washing machines. You know, I've I've seen that. And, certainly, you know, monitoring and observability tools.

You know, that's that's happening because I think lots of people and and and then just, you know, you you look at GitHub co pilot.

You know, which is another generative AI that helps code complete. And and I've seen a few of my capital d developer friends. I'm a script kitty basically, which is an insult to scripts and kitties everywhere. But I, you know, but I have friends who are real developers and watching them use co using co pilot it's reading the code base, their actual code base, and making assumptions about what function they're about to throw in there, how they're gonna implement that function, pattern they're gonna use.

I mean, it's making some really interesting and complex assumptions and then helping them code complete. And that's from a developer's standpoint. So, of course, the potential is there. The thing that strikes me though is, on the now it's less so today than it was three months ago.

But three months ago when Chachipity was really, really the new hotness and nobody knew what was happening, people were sort of losing their ever loving mind because, you know, it's gonna get, you know, everybody who writes is gonna be out of a job and everybody who does essays in school is going to be using this, and we'll have no way of knowing if it's plagiarism and whatever. And I'm not saying those risk were unfounded, but I think that they were overblown in the same way. And again, I'm gonna date myself when I was in elementary school, pocket calculators first came out. And in fact, late elementary school, early junior high is when Cassio made its first watch that had teeny tiny itty, bitty buttons for a calculator on the watch, and schools were losing their ever loving mind.

Nobody's ever gonna learn how to do again, nobody's gonna learn how to, you know, calculate. They're just gonna put it on the calculator and of course that never happened because you still have to know how to math to use the calculator. If I'm balancing my checkbook and I'm using the square root function, something is horribly horribly wrong, and it's probably not that my finances are very complicated. It's probably that I don't know how to math particularly well.

So what the calculator ended up proving was that it allows somebody like me who is not consistent in their math abilities to be more consistent and I see generative AI doing the same thing, but the problem is you have to know what you're doing and the first place and taking it.

Oh, yeah. Absolutely.

Yeah. T taking it back to the network thing. You know, if I use NLQ you know, natural language query, and I say, I'd like you to show me all of my layer two switches that has high CPU and, you know, are causing routes to collapse, which I know is not a thing. I'm just making up words.

Right? I'm I'm basically being chat GPT right now. But I have to I have to have a sense that the sentence I said is utter hogwash. I will use that word in lieu of something stronger.

And also, even if I phrase something correctly, oh, I wanna know all the routers that have high CPU and are in spin lock.

I have to be able to look at the results and understand whether I actually got back what I asked for.

None of none of the labor saving of NLQ of of the LLM has done anything to reduce my the onus on me to be a decent IT professional. What it might do though is help somebody who's a little bit newer ask a question that they understand the question but won't won't know how to phrase it correct particularly well and help them bridge that gap.

It's what I see. Or who don't necessarily know how to get the answer? Because that's ultimately the the real thing here is that the whole point is augmenting an engineer, not replacing engineer. And so there's an incredible amount of domain knowledge that's required for you to prompt the the the platform correctly and properly so that you don't get a ridiculous answer. I mean, you wanna know Why is my, why is Microsoft three sixty five slow in my Chicago office on the second floor of the building?

You know, that's that's very broad. So you don't need it. But, you know, to be able to understand the answer when it starts to talk about we're experiencing latency on this hop with this particular provider. Here are the IP addresses or something like that.

Right? It requires a lot of domain knowledge both to prompt correctly and to understand the answer correctly. What it's really doing is, again, it's an interface between a human being and the data set. And so that presupposes knowledge of, of the dataset, not a knowledge meaning an, of of knowing all of the data, but understanding the the forms and types of data, and then allowing the machine to do something that, we can do technically, but just dramatically faster.

Right. Dramatically faster. And, and so, you know, if you can afford a team of twenty PhDs from MIT, maybe they could do what an LMM and an LQ and all these things can do for you in a few weeks or months or I don't know. And, you know, they're identifying correlation and saying the correlation coefficient with these two things here or is this, and we We think that swinging p peers from data center a to data center b is causing latency over here with this application.

So you could they could start to figure that out. But honestly, a lot of engineers can as well. It just takes an inordinate amount of time. And so what we're doing is we're using this, this this approach to reduce MTTR.

That's always the case, right, reduce the meantime to resolution by augmenting a very operational practice by making something that's very manual, clue chaining, you know, troubleshooting whatever you wanna call it, programmatic.

And I think the really cool stuff that so that's the stuff that we're doing already right now. And we're doing it with some, a small subset of our customers who wanna try this out. Training the model and doing, you know, fine tune it for accuracy. Cool.

Long term. I'm really excited. That's cool enough. That is true. But I I am really excited about the things where we start to use the tool to identify correlation to derive insight that would be near impossible for a human to do.

So what you're Because of the vast volume and divergence diversity.

Right.

And and what you're doing now is you're calling out two things. You're calling out the the ease of use. Again, that natural language interface along with the data. But but they're they go together.

Right? Like the whole point of observability, you know, observability versus monitoring, right? I this is part of a lot of conversations I have is you know, Are you old school monitoring, are you either the new fancy newfangled observability at which point, you know, charity majors had explodes and everything like that because everybody is defining observability is completely different things. But you know, observability writ large tends to be interested in large disparate data sets and digging through things that a human cannot reason about.

In any way because there's just so much data in so many different directions. And so on the one hand, you have an observability system. Something that can take that large data and again slice it, pivot it, and reason about it. And then you're putting the LLM, the large language model interface in front of it so that you can ask questions without having to do promco or, you know, some sort of, you know, customized query language and and that can be incredibly powerful.

But I wanna make sure everybody who's listening recognizes that we are talking about two major things. One is the data itself and the engine on top of the data that can that can reason about it that can deal with that much data and slice it and dice it and then the language model on top of it. And what we Yeah. What we're getting to also is I heard this term a few times now. Prompt engineer.

Which I love. I I would be terrified if it became an actual job, but I think is a skill set prompt engineer, meaning somebody who understands LLM interfaces enough to write better prompts sooner so that you bring down the cycle time on asking a question of the LLM and getting the correct or the accurate or the or the desired response back because One of the things I see all the time, I, you know, watch video, YouTube videos or whatever about, you know, I typed this query in and it wrote my entire two thousand line program for me. And I'm thinking about that scene from the movie Sully where they've shown him the pilots who executed the maneuver to go back to LaGuardia.

And he just leans into the microphone and he says, how many times? And everyone's like, what do you mean? And he says, how many times did they have to practice that before the one that you just filmed. And the answer was seventeen.

They had to practice the maneuver seventeen times before they finally got it right and filmed it. And I see that with people interacting with chatgyny or any of the other LLM system is that the first question never gets you what you want. You've got, oh, no. No.

Not not not like that. I need you to do it like this. Okay. Yeah. But I need it to be shorter.

I need to be And finally, by the fifth seventh tenth twentieth iteration, you finally know how to ask the question the right way to get it. It's to quote a different movie because I apparently that's what we're doing from the Lego movie, you know, Batman when he's like, got it the first time. After he's thrown the batarang like fifty two more time. Like, first time.

So, you know, okay. Alright. Fine. You know, always post your w's.

But it is a an entire ecosystem of technology. Right? So we've been focused on chat We've been focused on large language models in general of AI because that's what, you know, it's top of mind since we talked about it today in that LinkedIn Live, But in the con the greater context of SDN, what its promises were a decade ago, and what's going on today, we are talking about utilizing all of these technologies to get us yet the next to the next step. You know, it's iterative, like anything else.

And so you mentioned, you know, we're talking about LMS, but an LLM is not going to graph out my OSPF database for me. It's going to be the interface with regard to language and predicting words and then summarizing, you know, maybe summarizing because things like that, And then it's gonna require those other technologies whether they be plug ins, like, you know, now Dolly is a plug in for chat GPT, but it could be any kind of plug ins network centric plugins, IT operations plugins, whatever. So that way, there is, the the the natural language processor is actually the interface between us and then yet other technology that is then going and interfacing with the data.

But ultimately, that's it. That's the whole software defined abstraction piece. And right now, we're talking about deriving, information, right, or, or identifying correlation, maybe, or seeing patterns. And that's all really, really cool.

That's a kind of a cornerstone of observability.

For operations, I for it to then even then, produce within the realm of probability or a confidence level, here is the solution. And then the next step after that, just things automating automatically, resolving themselves, through this entire workflow of many, many different top that's That's, very compelling. That's obviously very far off, but, you could see the beginnings of that all happening. Yeah.

The thing the thing I wonder about and I'm gonna ask you about is whether anyone will get to or be comfortable getting to a point where the system. This this, again, the the data sitting on top of an engine, they can reason about the engine sitting on top of a language model, where anybody would be comfortable with it basically autonomously dealing with everything. I I think that traffic routing is fairly, you know, it is fairly simple enough and every CCI listening to this is laughing their head off right now. Oh, yeah.

Sure. It's simple. Yeah, Leon. Whatever. You you had your CCNA in two thousand four. You know, but, I think that that routing traffic, looking at traffic patterns and making some changes, is one thing but I know people who refuse to implement alerts like autumn automation in their alerts because they're not comfortable that the problem is predictable enough to even respond with automation to relatively simple problem.

Like, you know, just, you know, interface configuration things and things like that. So I wonder whether our comfort level for autonomous reaction will ever increase to the point where we would actually let it drive unattended.

I think so. I do. And, I I'm I I bet there are a lot of people that disagree with me.

I've had that conversation many times. Both when I was a practitioner and then afterward when I was working for vendors. But I know some colleagues of mine that are still practitioners and and then customers of ours that say, what the the thing the the platform will automatically the network will fix itself? Yeah.

That's great. Do it. So this idea that I will only Yeah. I only want the, I want the alert.

I want the suggested remediation. That's awesome. By the way, if we stop right there, that's still a gigantic step forward. It's not like I'm knocking.

And I want that. But then I want the, you know, the button to be able to say, now push the configuration. So I can review and do everything. Maybe I incorporated into a change management workflow and submit it to a cab meeting or something like that. And so that is true. But I I do know enough, like, and and especially in smaller enterprises.

So let's say like a large school district where you have a small team of IT folks and maybe one network expert, one person who's managing all the windows or desktops, whatever. I don't know. You got a team of ten people, which sounds like a tiny little organization, but it might be fifteen thousand people in this in this school district. So so in an organization like that, oh, it's not mission critical because it's this or it's not e commerce, it's not fancy.

And it's like, oh, really? How about, connectivity to all the school buses that have, you know, a lte modem on the bus and they're sending metrics and and GPS back to the main office. And then there's, like, police and municipal services involved. That's not mission critical to you.

The lives of the fifty kids on that place. Come on. And so, yeah, and the two cops that are down in the main office, they have an office in the office. I mean, you see this stuff.

And so I I think in an organization like that, yeah, you say, hey, this platform will automatically, fix this, you know, these switch ports will automatically get reconfigured, or we'll re reroute this thing, you know, maybe maybe maybe we we reserve certain things for I need approval, but I absolutely think that there are engineers that would jump all over that. I I would, you know, and I get it. There's a trust relationship, but I think that with actual practitioners, saves certain, very, you know, mission critical, sort of, very sophisticated environments. I think I think there would be mass adoption very quickly.

And I think one of the key things to that is accountability within the system, meaning it's one thing to say my monitoring is collecting all this, you know, telemetry and then it's digesting it and the the language model is is looking into it and all of that stuff and then it's making changes. But can I do, a time machine effect? Can I roll back and say, alright, the network looked like this and then this thing happened and then this change was implemented as long as I can go back and review it and say, oh, I the assumption the system made? I see why it did that thing.

I think that that I'm comfortable enough to know Oh, I I fed it. I fed it wrong assumptions. I fed it. You know, not enough parameters.

I didn't set a threshold high enough. To say when this happens, you know, there's my circuit breaker pattern of something. I I think that's a key aspect of it. I mean, I realize that we are designing product as we talk on this podcast.

And it's really easy actually. I've never coded as well as I I'm doing right now, but it's I I think as long as you have that accountability within the system, a lot of more people would be comfortable with it. It's when I don't know what happened. I don't know why.

I I can't I can't look back. That's that's where I think people are afraid things might go. Because we can't when I use Daldali, when I use Chachipity, when I use those things, I can't see what's happening under the hood. And I think that people worry that that's gonna be the default experience behind, you know, I can't see why what was it?

Temperature, in the in the LinkedIn live, was talking about temperature to figure out the next word and the threshold to figure out the next word. And if I can't see the temperature settings, if I can't see why it decided to use the word manhole cover instead of hamster instead of, you know, sandwich.

I I don't understand why it's doing this and I can't trust it.

Yeah. Yeah. And you can't interact with it anymore because you don't know how to elicit the response that you want. You know?

And when you say trust, we're talking about a a matter of accuracy and, confidence that it's gonna do the right thing or, you know, push the right config and solve the problem. But we kinda do that already. Don't we? I mean, SD WAN will reroute traffic for you without you telling it to.

It does it automatically based on whatever metrics and and, whatever whatever whatever it's using to test the quality of various links, and sometimes it's not just the links, but entire path between source of destination, and and then it reroutes traffic.

We have other other technologies that we use that that are similar in the security field, will shut down an interface if there's, thresholds, whether they're dynamically created thresholds by pattern recognition and by baselining. So that's kinda statistical analysis and some, you know, basic ML stuff.

So we do that there. Granted, it's not, you know, reconfiguring data center, but it is it is an action of pushing a config shutting down an interface or something like that.

I One of the tenants of intent based networking was to be able to use a certain type of more natural language to tell the system, to, that this is what the intent of of this configuration of this network is supposed to be, a reference architecture, if you will.

And then if there's any deviation, the the network will will heal itself. So so we're we're already moving in this direction. And that's why I really do believe that engineers out there would be quick to adopt something that actually does push config and there's a trust relationship. And and and I think very quickly, you're gonna see that, if there's a problem, if if trust is broken. Nobody's gonna buy the product, and it's not gonna you're not gonna see it as a common, platform deployed in all the enterprises, around the world.

So there there is that market control, I think.

Right.

But I don't know. Do you think that that's what SD SDN software defined networking is all about?

It's been a long time heard anything about it, maybe we should start writing some blogs in in in this kind of Right.

Well, I mean, I again, I think SDN failed. SDN failed to define itself clearly enough to avoid being marketed to death.

And so, it became anything that anybody wanted it to be. It became anything the marketers wanted it to be. And therefore, technologists stayed away in droves. I think that if somebody put a stake in the ground and said, this is SDN, even if this is, you know, company X brand SDN.

All rights reserved. Trademark.

I think that that would help IT practitioners feel more comfortable that they were getting what they thought they were getting.

I to your point about SD WAN working, I think that there's a relatively small segment of the IT practitioner space, let alone the network engineer space that deals with SD WAN on a regular basis. So it's somewhat invisible. I also know that whenever anything automatic happen, look, I've I've had web sites that have ended up on black lists for, email spam accidentally or whatever. And and I know that when something is misconfigured incorrectly when it's when it's misconfigured and I and I personally feel wronged by it.

People get upset really, really fast. And and, you know, they're up in arms about it. And I think that if you have, again, a monitoring and observability system that is feeding into your network configuration, the opportunity for a mistake to destroy all trust is really is really large.

Yep.

And I think that SD WAN SD WAN is unique enough.

And limited enough limited in terms of the audience. I I would argue that the the CTO doesn't re might know that SD WAN is happening in their organization, but it may not even know who's responsible for it. Let alone where it happens or which devices or any of that stuff. Yeah. I I think there's a a small cadre of people who understand it enough to do anything with it.

Everyone else may point to it as a look, we have SD WAN.

It's amazing. I don't know why.

Particular is a outgrowth of one of the definitions of SDN, the disaggregation of the control plane and the device. And so now you have you know, your control plane well, I mean, there is a control plane that's distributed so the devices can make local decisions and hardware, but then you have your central brain, which is some controller SD WAN controller, in your data center or in the cloud. And so there is, you know, policy that's pushed from there. And so there's a lot of benefits.

So I think SD WAN specifically falls under that sort of definition of of SDN. Where a lot of folks were harping on that one definition, the disaggregation of the software and the hardware, the control plane, and the data plane, that kind of thing. And you saw, you saw that happening at the switch level. You saw that happening out here at the the router, the WAN.

Right? I started to see that at the campus level with certain with certain, vendors that had entire overlays built on VXLAN and and DNS and things like that. And so, but that but that's just one sliver. Yeah.

And so that's why that's why, again, it it was an ambiguous nebulous term. We weren't able to pin down, so I completely agree with you there. And, but I I have to say, you know, it it still makes me wonder sometimes now that I hear LLMs and and generative AI all the time, if that's also, likewise, just a term that the marketing folks use and that practitioners roll their eyes at, and it's gonna disappear. But, you know, that that thought is in my head, but I don't know Liam.

I think maybe I mean, we're just seeing too many real practical benefits.

In in the rest of the world too, but but specifically in technology for me to say it's just I I've watched I've watched you know soa and, oh god.

So many different standards and ideas that took the marketing space in IT by storm and then just, you know, fell away because nobody backed it up or nobody could define it or whatever. And and I think SDN fell prey to that, but you're right.

There's already been the AI winter.

So we've already been through that. And so now we're looking at an actual implementation of AI. So I think it's gonna turn into something whether it remains what we see today is entirely a different question, but I think it's gonna end up being it's it's gonna end up being something. It's gonna end up being something fairly useful.

But it is going to progress.

Like like everything like everything does around here and we'll be here another five years saying. Remember when we were talking about, you know, I think I called it chat Jiffany back at the time because I thought I was being witty and it was actually just really tired and old. But, you know, I mean, we'll we'll be rehashing it. It'll morphed into something else.

Yep. Yep. But I I do think that this is another step forward in that, though I called it ambiguous and nebulous this whole thing about SDN, I do think it's another step forward within that realm of software defined networking and and adding intelligence to what we're doing that extends the human ability that augments the engineer. I really think that. So this is really cool stuff.

And Yeah.

Maybe I will write a blog post about SDN Maybe, of course, you will.

Of course, of course, we will because it is what we do. We write things and we talk and we drink and we know things. That's our job.

Yep. Yep. In that order and because it tends to be that I know more things after after a couple of years and not prior. Yeah.

But, Wayne, I was good to talk to you. I appreciate it. We're gonna have you on about, I don't know, a million more times because there's so many things to talk to you about now that I'm getting to know you. So, I'd like to have you.

It is absolutely delightful. I'm really I'm happy to be part of this and happy to be part of the the bigger capital t this that is Kentic. And I just appreciate you making space for me today on the show.

Absolutely.

So, if somebody wants to reach out to yell at you about your opinions on SDN.

How can they do that online?

I I welcome it because it's you know, a a rich source of conversation and and no right answers and no wrong answers. So my name is Leon Adatto and Datto spelled a d a t o and that's how you can find me. I'm Leon Adatto on almost every platform on the bird site that I will not name. I'm very, very infrequently there, but on blue sky and on mastodon and on LinkedIn, all a GitHub, but you can find me all those places. And also, I have a personal blog, a Datto Systems, which is my last name in the word systems dot com. You can find me there too. And of course, you can find me leon at kintech dot com dot com as well.

Very good. Excellent.

And, you can me online. You can search my name in LinkedIn. Find me on Twitter network underscore fill. My blog network fill dot com.

And, now if you have an idea, an episode of telemetry now, or you'd like to be a guest. I'd love to talk to you. So please reach out to telemetry now at kentic dot com. Love to talk to you about it.

Anyway, For now, thanks for listening. And until next time. Bye bye.