Category: Tags
netlab
The netlab tool will help you be more proficient once you decide to drop GUI-based network simulators and build your labs using CLI and infrastructure-as-code principles.
SD-WAN
Software-Defined WAN (SD-WAN) is the second “software-defined” marketing attempt (after the original SDN) to dress a conglomerate of old technologies into shiny new clothes. Even Wikipedia article promotes some of the usual software-defined hype, quoting Network World claim that:
SD-WAN simplifies the management and operation of a WAN by decoupling the networking hardware from its control mechanism. This concept is similar to how software-defined networking implements virtualization technology to improve data center management and operation.
Is It Real?
Want to know how real those claims are? Start the journey with this series of myth-busting blog posts:
- Software-Defined WAN:Well-Orchestrated Duct Tape? (2015)
- Routing Protocols and SD-WAN: Apples and Furbies (2015)
- Do Enterprises Need MPLS? (2016)
- Lack of Fast Convergence in SD-WAN Products (2018)
- Lock-In and SD-WAN: a Match Made in Heaven (2019)
- Impact of Controller Failures in Software-Defined Networks (2019)
- Fast Failover in SD-WAN Networks (2020)
Does SD-WAN make sense? Sure:
Need More Details?
I covered the basics of SD-WAN in Choose the Optimal VPN Service and SDN Use Cases webinars.
Pradosh Mohapatra described the basics of SD-WAN and its typical components and architectures:
- What Is SD-WAN?
- SD-WAN Reference Design
- SD-WAN Backend Architecture
- SD-WAN CPE Architecture
- Security Aspects of SD-WAN
Want to know more about Cisco’s SD-WAN solution (formerly known as Viptela)? Enjoy David Peñaloza Seijas’ deep dive into its architecture and implementation details:
- Going Beneath the Cisco SD-WAN Surface
- Cisco SD-WAN Fundamentals and Definitions
- Cisco SD-WAN Solution Architecture and Components
- Cisco SD-WAN Routing Goodness
- Cisco SD-WAN Onboarding Process
- Cisco SD-WAN Policies and Centralized Magic
- Cisco SD-WAN Policies Review
- Cisco SD-WAN Routing Design
- Cisco SD-WAN Site Design
- Cisco SD-WAN Policy Design
Real-Life SD-WAN
SD-WAN sounds great, but does it work as expected? Maybe not:
- SDN, SD-WAN and FCoE on Gartner Networking Hype Cycle (2015)
- SD-WAN Reality Gap (2019)
- Real-Life SD-WAN Experience (2019)
- Worth Reading: SD-WAN Scalability Challenges (2020)
- Feedback from Another SD-WAN Fan (2020)
Is it secure? Some products seem to be nothing more than a bunch of open-source component glued together with clueless Python code:
- Security Aspects of SD-WAN Solutions (2018)
- SD-WAN Security Under the Hood (2019)
- SD-WAN Security: A Product Liability Insurance Law Would Certainly Help (2020)
- Another SD-WAN Security SNAFU: SQL Injections in Cisco SD-WAN Admin Interface (2021)
Some service providers want to use SD-WAN to offer managed services. Not surprisingly, some people1 don’t find that a good idea:
- SD-WAN: A Service Provider Perspective (2020)
- Managed SD-WAN Services (2022)
- Challenges of Managed SD-WAN Services (2022)
Then there are some technical details vendors love to gloss over:
- Does Unequal-Cost Multipathing Make Sense? (2021)
- Topology- and Congestion-Driven Load Balancing (2021)
Does it work within a public cloud? Yeah, sort of… with a few challenges:
Want Even More?
Love marketing-related rants? Here are a few:
- Some Ridiculous SD-WAN Claims (2015)
- What Is Software-Defined Security? (2016)
- This Is Why I’m Not Doing SD-WAN Webinars (2016)
- The Ever-Increasing Complexity (2017)
- SD-WAN Vendor Landscape (2019)
Last, but definitely not least, you might enjoy these (more esoteric) solutions:
- DLSP – QoS-Aware Routing Protocol on Software Gone Wild (2015)
- Changing Cisco IOS BGP Policies Based on IP SLA Measurements (2019)
- Overlay Networking with Ouroboros on Software Gone Wild (2020)
- Scalable Policy Routing (2021)
Blog Posts I Forgot to Categorize
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Including those working for said service providers or their customers ↩︎
AI
Artificial Intelligence (AI) and Machine Learning (ML) are the next big hype in networking following Software-Defined Everything and Intent-Based Everything. Like with the previous hype bubbles it’s worth figuring out
- How much of the hype is real (TL&DR: not much)?
- Whether the technology is ready to be used in production networks (TL&DR: some of it)
- How you could use the technology to make your life easier
How Real Is It?
Like with the previous hype tsunamis I’ll do my best to help you figure out the answers to the above questions with a hefty dose of skepticism and snark1, starting with:
I also decided to “kick the tires” and document my (often less-than-stellar) experience with the most-overhyped products:
- Real-Life Not-Exactly-Networking AI Use Case
- ChatGPT on BGP Routing Security
- Kicking the Tires of GitHub Copilot
- Building a Small Network with ChatGPT
- ChatGPT Explaining the Need for iSCSI CRC
- Source IP Address in Multicast Packets
AI/ML in Networking: The Good, the Bad and the Ugly
Javier Antich created a wonderful AI/ML in Networking in 2021. If you know nothing about AI/ML and wonder whether you should care about it, you MUST watch these videos from his webinar:
- Introduction to AI/ML Hype
- Machine Learning 101
- Machine Learning Techniques
- Use Cases for AI/ML in Networking
- The Long Tail of AI/ML Problems
- Ugly Challenges of Using AI/ML in Networking
In 2023, Javier published a book covering the same set of topics in way more details. I would highly recommend you read it if you want to know more.
What Others Are Saying
I keep collecting interesting articles talking about AI in general and (lately) ChatGPT. I found these interesting enough to mention them in worth reading blog posts:
- Machine Learning Explained (2020)
- AI Makes Animists of Us All (2022)
- The AI Illusion (2022)
- Collections: On ChatGPT (a Historian Perspective) (2023)
- Putting Large Language Models in Context (2023)
- The Dangers of Knowing Everything (2023)
- Building Trustworthy AI (2023)
- Cargo Cult AI (2023)
- Building Stuff with Large Language Models Is Hard (2023)
- Worth Reading: AI Does Not Help Programmers (2023)
- Eyes that glaze over. Eyes like saucers. Eyes that narrow. (2023)
- Networking for AI Workloads (2023)
- Looking Inside Large Language Models (2023)
- Where Are the Self-Driving Cars? (2023)
These are not bad either:
- What Is ChatGPT Doing … and Why Does It Work?
- We Can’t Build a Hut to the Moon
- The Delusion at the Center of the A.I. Boom (aka AI Solutionism)
- ChatGPT and Chemistry
- Cal Newport on ChatGPT
- Ruby Development with ChatGPT
- ChatGPT Is Your New Intern
- Using ChatGPT as a Technical Writing Assistant
- Why OpenAI is the new AWS
- Overemployed Hustlers Exploit ChatGPT To Take On Even More Full-Time Jobs
Finally, a few real-life uses of large language models:
- An Exploration of Embeddings and Vector Databases
- How GPT and LLMs will affect documentation
- I Built an AWS Well-Architected Chatbot with ChatGPT
- Building Boba AI – how to build a custom user interface in front of a large language model.
- Using Langchain to interact with ChatGPT
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Please don’t blame me for pointing out the ever-lasting validity of Sturgeon’s law. Contrary to what some people think, I’m not trying hard to pick up dismal examples of AI failures, I’m just good at looking in the wrong places. Also, I’m too old to be wearing rosy glasses and drinking Kool-Aid. ↩︎