Networking
Networking insights
Filter by Content types
- No options
506 results found
Cisco (Viptela) SD-WAN
This Learning Path is designed to Discover and Experience Cisco (Viptela) SD-WAN. The lab environment that supports this Learning Path is virtual and dedicated to each individual consumer.
Learning Path
Border Gateway Protocol (BGP) Foundations
This learning path on foundational Border Gateway Protocol (BGP) provides a comprehensive introduction and deep dive into the core aspects of BGP, the backbone of the internet's routing architecture. It starts by explaining the basics of BGP, including its purpose, operation, and fundamental concepts such as Autonomous Systems (AS), BGP sessions, and the BGP routing table. The series progresses to cover more advanced topics, such as BGP path selection, route advertisement, and the implementation of various BGP attributes like Local Preference, AS Path, and MED. Through practical examples, configurations, and troubleshooting scenarios, viewers gain a thorough understanding of how BGP facilitates global data exchange and the techniques network engineers use to optimize and secure BGP networks. The series aims to equip network professionals with the knowledge needed to manage and optimize BGP in real-world environments, emphasizing best practices and common pitfalls.
Learning Path
Cisco ACI
Cisco ACI is a policy-driven CLOS or Spine/Leaf based switching fabric utilizing layer 3 ECMP routing in the underlay and VXLAN encapsulation in the overlay to transport layer two and layer three traffic East/West across the fabric and North/South in and out of the fabric. ACI consists of the Application Policy Infrastructure Controller (APIC), a centralized controller that manages all aspects of the ACI fabric. The leaf switches are ToR switches that provide connectivity between servers and external networks, and the spine switches are Layer 3 switches that provide ECMP high-bandwidth connectivity between leaf switches. An ACI fabric can be expanded East/West by adding leafs, cabling to the spines, and registering them. ACI was designed to operate as "One Big Switch" (like a chassis-based NEXUS 7K) with the controllers acting like the Supervisors, the spines as fabric modules, and leafs acting as blades. We can decouple these elements from the chassis and place them anywhere in the data center by taking this approach. The leafs (blades) can be placed anywhere in the data center, so you are not limited to a chassis.
Learning Path
Cisco Catalyst (Viptela) SD-WAN
This Learning Series is designed to Discover and Experience Cisco Catalyst (Viptela) SD-WAN. Cisco Catalyst (Viptela) SD-WAN is a solution that provides secure and scalable connectivity for enterprises by optimizing and simplifying the management of their wide area networks.
Learning Series
Cisco NEXUS Dashboard
Previously, the Cisco day 2 operations suite of MSO, NAE, NIR, and NIA ran separately on computing as a .ova in vSphere or on the APIC as an application. The architecture never allowed for sharing data between the apps or correlations with errors and telemetry views of packet loss. With the roadmap moving, we had applications and a shared data lake for all applications to draw from and correlate between application errors, changes to the policy, and deep flow telemetry, all visual as an epoch.
For all the applications to run and have sharable databases, Cisco NEXUS Dashboard (ND) was created. The ND platform allows all the Cisco Day 2 apps and third-party applications to run on a single appliance. Secondly, the ND has to be an expandable CPU and storage-intensive platform; today, the platform can scale with 3 master nodes and 4 worker nodes with the apps and their data residing on the ND cluster. As ND matures, more ND servers can join the cluster, and they can be separated regionally if within TTL requirements to distribute applications and provide DR strategies.
Learning Path
Building VXLAN Fabrics using Cisco DCNM and NEXUS Dashboard Fabric Controller
Data Center Network Manager (DCNM), now superseded by Nexus Dashboard Fabric Controller (NDFC), is Cisco's version of an EVPN fabric controller. DCNM, or NDFC, has three essential primary components. Spines that act like physical aggregation points for all of the leaves. Leaves provide endpoint aggregation and link to the spine. The last essential component is the controller, which in the case of DCNM is a single device or HA pair, and for NDFC, it is a Nexus Dashboard cluster with a loaded NDFC application.
In most cases, DCNM or NDFC will utilize OSPF for the underlay and VXLAN EVPN with MP-BGP as the overlay on Nexus 9000 switches. These fabric controllers are easier to use than similar fabric technologies on smaller to medium size networks, as most variables work out of the box without needing to change them. To what is known in the industry as a point and click your way to happiness.
Learning Path
Network Engineering Foundations
This Learning Series covers the fundamentals of network engineering. Through hands-on labs and real-world examples, you will develop the practical skills necessary to build and maintain robust networks. It is perfect for aspiring network engineers, IT professionals, or anyone interested in understanding the backbone of today's interconnected world.
Learning Series
WWT Helps a Large Midwestern Manufacturer Escape Wireless Vendor Lock-In
When a large midwestern manufacturer encountered significant challenges with their existing wireless OEM, WWT stabilized the company's wireless network and helped them select, test and deploy a second OEM solution for vastly improved performance as well as simplified deployment, use and management.
Case Study
• Feb 23, 2024
Using PFC and ECN queuing methods to create lossless fabrics for AI/ML
Widely available GPU-accelerated servers, combined with better hardware and popular programming languages like Python and C/C++, along with frameworks such as PyTorch, TensorFlow and JAX, simplify the development of GPU-accelerated ML applications. These applications serve diverse purposes, from medical research to self-driving vehicles, relying on large datasets and GPU clusters for training deep neural networks. Inference frameworks apply knowledge from trained models to new data with optimized clusters for performance.
The learning cycles involved in AI workloads can take days or weeks, and high-latency communication between server clusters can significantly impact completion times or result in failure. AI workloads demand low-latency, lossless networks, requiring appropriate hardware, software features, and configurations. This article will explain advanced queueing solutions used by all the major OEMs in the Network Operating Systems (NOS) that support ECN and PFC.
Article
• Jun 25, 2024
Networking Priorities for 2025
An actionable roadmap for focusing your networking initiatives.
WWT Research
• Jan 1, 2025
Aruba Wireless and Clearpass Foundations Lab
This Lab guide is intended for administrators who are responsible for deploying and configuring AOS 8 solutions. Participants should have at least a basic understanding of WLAN concepts. It is assumed that participants have at least a working understanding of fundamental wireless concepts as well as Aruba technology.
Foundations Lab
• 207 launches
Revving Up ECMP Routing for AI/ML Workloads
Dive into the world of high-performance networking. In this article, you will discover solutions to optimize Ethernet for AI/ML and HPC environments, ensuring seamless GPU synchronization and enhanced network performance.
Blog
• Jan 7, 2025