What is the new service provider network? This is the network modernization process all service providers need to go through. Service Providers cannot continue to build networks the same way they have for over 20 years, and still maintain a valid business.

DCI is going through something and this something is called AI. The three changes that AI made to DCI are capacity, performance, and one environment and layer three capabilities.

What’s the importance of latency in AI networks. AI networks introduce new challenges that need different treatments of latency.

Are autonomous networks coming soon? We’re taking baby steps, starting with islands of automation and we’re going to automate small, mundane tasks by helping operators develop CI/CD pipelines that allow them to take innovation from their lab to a network digital twin and eventually into production in a much faster way.

DES, DSF, DDC – Comparing Scheduled Fabric Arista recently launched their DES solution, the Distributed Etherlink Switch, which is essentially an end to end VOQ system – a large scale chassis. The approach is basically saying we should put as much logic as we can on the switch side. The switch will handle all the congestion control, all the reordering of packets, the load balancing of packets, which are all necessary for AI networking. So why choose DriveNets DDC over those solutions?

We have an issue with resolving the AI fabric, or an AI networking problem with large clusters of GPUs usually used for training. This episode looks at the issue with resolving the AI fabric and explores how an Ethernet based solution can resolve this – by building a chassis which is distributed (a disaggregated, distributed chassis). This approach has no packet loss, is lossless and is a fully scheduled fabric, but without the scale limitation of a chassis.

What are the new automation sets coming to DriveNets Network Orchestrator? Autoboot profiles helps you bootstrap new white boxes from the factory. Smart Rollout is a brand new feature and it allows you to push software into the entire network after configuring just a few parameters. The Single Source of Truth integration seamlessly integrates the inventory that we have in DNOR into NetBox, or or other inventory systems.

Failure recovery is a very big issue when it comes to AI clusters because there are always failures and when the failure come, it’s a big thing because you need to stop the calculation, go back to the last checkpoint. You lose a lot of time and money and resources that are spent idle and and wasted time. And the networking part is crucial in order to create a fail.

Congestion demands attention, otherwise it can result in higher latency and packet loss. The main dilemma around congestion is around avoidance or mitigation. We’ll look at how scheduled fabrics make sure that AI infrastructure is lossless and predictable rather than bringing additional technologies to mitigate the congestion, but do not avoid it

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 operatio...

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.

DriveNets Network Cloud, changes the operational and economic model of the network, allowing it to scale capacity and services much faster while increasing service provider profitability. Watch CloudNets at https://drivenets.com/resources/cloud-nets/special-why-include-drivenets-in-your-next-network-rfp/