Hi and welcome back to CloudNets, where networks meet cloud.
And today we're going to talk again about AI networking. And this time we have Run, our chatbot, that will provide the solutions for the challenges we mentioned last time.
So, Run, let's dive into it right away.
We had three challenges.
What are the solutions?
All right, so challenge number one that we had is that the network needs to be very flexible and very much connected to the outside world.
First off, flexibility Ethernet and connected to the outside world, Ethernet. 600 million ports of Ethernet are deployed.
Each and every cannot be wrong. They cannot be wrong, exactly.
You don't need a gateway because the
Internet is also interfacing to that network via, again, that same standard Ethernet. Ethernet can be built to any scale of a network that you need.
So in that sense, Ethernet is kind of a classic solution to this challenge.
As opposed to proprietary interfaces that in some cases are used in the back end.
The first time that you say proprietary, that closes the open.
Yeah.
Okay, so let's go to challenge number two.
All right, scale.
Yeah.
Challenge number two was about very large scale, and performance, obviously performance at scale.
So in this case, a DDC type of a solution provides a fabric, whereas other solutions provide a network.
Network has an impact.
Okay.
Network has network because it has multiple hops.
Exactly.
You want a single chassis, many, many cables.
Exactly.
And network has nodes, network junctions. And these junctions bring in traffic from multiple locations onto multiple destinations.
And this crisscross or mishmash of traffic flows is an impact.
And this impacts the application.
Remember, application is king.
You don't want to hurt the application. So those networks are not scheduled.
There suffer, packet loss and jitter, et cetera, et cetera, which results in higher JCT.
They are networks.
Networks are networks.
Exactly.
And DDC is a fabric.
Fabric is a fabric.
It's that simple, right?
Okay, that's challenge number two.
Okay, so we want DDC. We want DDC.
Now the question. Which DDC?
And that was, in a way, challenge number three.
What you would like for your AI network is something which is robust, something which is reliable, something which is field proven.
And this is exactly where DriveNets comes into play with DDC AI-3.0, version 3.0 of the solution.
Actually, this is the same DDC that we used in AT&T only optimized for AI networking.
Hence the 3.0.
It's actually the third generation.
And the second generation, which is basically the same architecture, is field proven.
We talked about AT&T numerous times.
This is what's running the core of AT&T's network in America.
Right?
So it's field proven up to almost 700 terabytes per second and 3.0 will have and more.
Exactly.
So you can actually pull this up to 32,000 endpoints, Ethernet endpoints.
So that's how large the cluster, how many GPUs you can interconnect directly to one fabric.
That's huge.
That's massive.
Okay.
And for sure, a lot larger than any chassis.
Not the same game. Not the same game.
Okay, great.
So now we are at ease because those main challenges of yeah, we solved, are resolved.
So just to recap!
The first one was to have a flexible and online architecture, which means you want an open Ethernet standard and not some kind of proprietary interface or protocol in the back end network.
The second one is that you want scale and performance and performance at scale. And this is where the DDC fabric, which is scheduled and predictable comes into place.
Unlike nodes and networks that bring in a lot of jitter and a lot of packets loss and nothing that resemble predictability.
And the third challenge is having a field proven rock solid solution. And after we've established that we want a DDC, we now know that we need DriveNets DDC because this is the only DDC that's actually field proven. AT&T and many others are already using it.
Thank you very much for resolving those questions.
Thank you for watching.
See you next time on CloudNets.
Thank you very much.
Cheers.
