Now and then, someone rediscovers that IS-IS does not run on top of CLNP or IP and claims that, therefore, it must be a layer-2 protocol. Even vendors’ documentation is not immune.
Interestingly, most routing protocols span the whole seven layers of the OSI stack, with some layers implemented internally and others offloaded to other standardized protocols.
Now and then, someone rediscovers that IS-IS does not run on top of CLNP or IP and claims that, therefore, it must be a layer-2 protocol. Even vendors’ documentation is not immune.
Interestingly, most routing protocols span the whole seven layers of the OSI stack, with some layers implemented internally and others offloaded to other standardized protocols.
Once again this year, Cisco Live US 2024 will take place in Las Vegas, Nevada, from June 2 to 6, 2024. I’m already registered and I’m looking forward to it! This year will be my 11th time attending Cisco’s annual conference in person, in both Europe and the United States. Straight to the point I’ve already written a few posts in previous years about what’s interesting to see and do at Cisco Live, and whether the conference is worth attending in person (and of course, it’s worth it!). This…
The post Cisco Live 2024 – an Englishman in Vegas appeared first on AboutNetworks.net.
As we all recover from NVIDIA’s exhilarating GTC 2024 in San Jose last week, AI state-of-the-art news seems fast and furious. Nvidia’s latest Blackwell GPU announcement and Meta’s blog validating Ethernet for their pair of clusters with 24,000 GPUs to train on their Llama 3 large language model (LLM) made the headlines. Networking has come a long way, accelerating pervasive compute, storage, and AI workloads for the next era of AI. Our large customers across every market segment, as well as the cloud and AI titans, recognize the rapid improvements in productivity and unprecedented insights and knowledge that AI enables. At the heart of many of these AI clusters is the flagship Arista 7800R AI spine.
In its most rudimentary terms, a digital twin in the realm of networking is the concept of emulating or simulating a like-for-like topology of a production...
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netlab release 1.7.0 added the fabric plugin that simplifies building lab topologies with leaf-and-spine fabrics. All you have to do to build a full-blown leaf-and-spine fabric is:
For example, the following lab topology builds a fabric with Arista cEOS containers having two spines and four leaves:
netlab release 1.7.0 added the fabric plugin that simplifies building lab topologies with leaf-and-spine fabrics. All you have to do to build a full-blown leaf-and-spine fabric is:
For example, the following lab topology builds a fabric with Arista cEOS containers having two spines and four leaves:
During the load balancer deployment process, we define a virtual IP (a.k.a front-end IP) for our published service. As a next step, we create a backend (BE) pool to which we attach Virtual Machines using either their associated vNIC or Direct IP (DIP). Then, we bind the VIP to BE using an Inbound rule. Besides, in this phase, we create and associate health probes with inbound rules for monitoring VM's service availability. If VMs in the backend pool also initiate outbound connections, we build an outbound policy, which states the source Network Address Translation (SNAT) rule (DIP, src port > VIP, src port).
This chapter provides an overview of the components of the Azure load balancer service: Centralized SDN Controller, Virtual Load balancer pools, and Host Agents. In this chapter, we discuss control plane and data plane operation.
Figure 20-1 depicts our example diagram. The top-most box, Loadbalancer deployment, shows our LB settings. We intend to forward HTTP traffic from the Internet to VIP 1.2.3.4 to either DIP 10.0.0.4 (vm-beetle) or DIP 10.0.0.5 (vm-bailey). The health probe associated with Continue reading
In a previous post, I discussed how Maximum Flow problems can be used for network optimization. We focused on a scenario where demands were already routed in the network, and our objective was to determine the maximum demand that could be handled between a given source and a destination metro. We solved this problem by calculating the residual bandwidth for the graph, creating fake demand nodes for each metro with high-capacity edges to avoid them being bottlenecks, and applying Dinic’s algorithm between the source and the destination metro. This is also called a Single Commodity Flow Problem.
We then extended the problem to consider two metros sending traffic to the same destination sink and used the Network Simplex algorithm to determine the maximum traffic the network could accommodate. This is also known as a Multi Commodity Flow Problem. Finally, we validated our findings by routing the results through a network model.
In this post, we will discuss another constraint-based problem called Minimum Maximum Link Utilization (MMLU). The primary goal of MMLU is to route traffic demands in a network to minimize the maximum link utilization. In other words, we aim to distribute the traffic evenly across the network links to Continue reading
Every time someone tries to persuade you to buy (expensive) big-buffer data center switches, take an antidote: the Things we (finally) know about network queues article by Avery Pennarun.
Every time someone tries to persuade you to buy (expensive) big-buffer data center switches, take an antidote: the Things we (finally) know about network queues article by Avery Pennarun.