Why is cell-based fabric is so much better than the alternative when it comes to Ethernet for AI fabric or AI infrastructure.

Chassis and Clos, which is better?
From the perspective of scale, absolutely Clos topology outranks a chassis. From an operational standpoint, a Clos topology would mean that you will need to manage a lot of boxes and that's a bigger headache than managing a single device, a chassis, one stop shop. But from a Capex standpoint, a Clos topology prevails over chassis. Looking at the alternative, Distributed Disaggregated Chassis (DDC), it takes the same scalability factor of a Clos, the operational aspects of a chassis and even when you look at the cost side of things, from an operational standpoint, DDC operates as a single device. Then from a Capex standpoint, DDC operates or works as white boxes.

We're going to talk about KDDI, one of Japan's major Tier 1 operators. They recently announced that they implemented DriveNets Network Cloud solution in its network and they are very satisfied. They are talking about huge TCO advantages, about 46% saving in power and 40% savings in rackspace, etc. 

Let's hear about KDDI and why is this deployment, a major deployment, of Network Cloud so important?

How can a disaggregated network solution provide benefits in terms of Total Cost of Ownership to service providers in a long term perspective

What are three things we can learn about Network Cloud in real world network operations.

Today we're going to talk about recovering from major faults, those things that keep you up at night.

DriveNets hosted a panel for analysts and media at MWC23 in order to explain its work and hear from industry leaders on progress for disaggregated networking. Members of the panel included Igal Elbaz, SVP and network CTO at AT&T;  Jean Louis Le Roux, executive vice president for International Networks with Orange; Cayetano Carbajo, VP for core, transport and service platforms with Telefonica.

Today we're going to talk again about AI networking, and we will provide the solutions for the challenges we mentioned last time. With the fast growth of AI workloads, network solutions need to be ready to resolve issues including  having a flexible and online architecture, being able to  scale and maintain performance at scale, and  having a field proven rock-solid solution.

What are the challenges of AI Networking? 

We’re going to talk about AI, and specifically about AI networking. we want to talk about the challenges behind the AI infrastructure, because AI ML training, it’s very compute, intense task. 

What are the main challenges in AI networking that companies like the Hyperscalers that are going into this market now needs to resolve? 

We’re talking about some myths about disaggregated distributed chassis or Network Cloud. The myth about Network Cloud and this architecture in general is just a one off. The myth about the operational headache. And the myth about that it’s a fairly complex architecture.