Continuing my archeological explorations, I found a dusty bag of old QoS content:

• Queuing Principles
• QoS Policing
• Traffic Shaping
• Impact of Transmit Ring Size (tx-ring-limit)
• FIFO Queuing
• Fair Queuing in Cisco IOS

I kept digging and turned out a few MPLS, BGP, and ADSL nuggets worth saving:

I wanted to test routing protocol behavior (IS-IS in particular) on partially meshed multi-access layer-2 networks like private VLANs or Carrier Ethernet E-Tree service. I recently spent plenty of time creating a Vagrant/libvirt lab environment on my Intel NUC running Ubuntu 20.04, and I wanted to use that environment in my tests.

Challenge-of-the-day: How do you implement private VLAN functionality with Vagrant using libvirt plugin?

There might be interesting KVM/libvirt options I’ve missed, but so far I figured two ways of connecting Vagrant-controlled virtual machines in libvirt environment:

Some networking engineers renew their ipSpace.net subscription every year, and when they drop off the radar, I try to get in touch with them to understand whether they moved out of networking or whether we did a bad job.

One of them replied that he retired after building a fully automated site deployment solution (first lesson learned: you’re never too old to start automating your network), and graciously shared numerous lessons learned while building that solution.

Recording the same content for the third time because software developers decided to write code before figuring out what needs to be done is disgusting… so it took me a long long while before I collected enough willpower to rewrite and retest all the examples and re-record the Getting Operational Data section of Ansible for Networking Engineers webinar.

The new videos explain how to consume data generated by show commands in JSON or XML format, and how to parse the traditional text-based show printouts. I dropped mentions of (semi)failed experiments like Ansible parse_cli and focused on things that work well: TextFSM, in particular with ntc-templates library, pyATS/Genie, and TTP. On the positive side, I liked the slick new cli_parse module… let’s hope it will stay that way for at least a few years.

On a totally unrelated topic, I realized (again) that fail fast, fail often sounds great in a VC pitch deck, and sucks when you have to deal with its results.

Someone pointed me to a high-level overview of Google’s Spanner database which included this gem:

A second refinement is that there are many other sources of outages, some of which take out the users in addition to Spanner (“fate sharing”). We actually care about the differential availability, in which the user is up (and making a request) to notice that Spanner is down. This number is strictly higher (more available) than Spanner’s actual availability — that is, you have to hear the tree fall to count it as a problem.

In other words, it doesn’t matter if your distributed database fails if its user are also gone. Keep this concept in mind every time you’re designing a high availability solution – some corner cases are simply not worth solving.

After figuring out what business problem you’re trying to solve and what the users expect to get from you it’s time for the next crucial question: should you buy a shrink-wrapped product/solution or build your own? I addressed that question in the third part of Focus on Business Challenges First presentation.

Not surprisingly, the same dilemma applies to network automation solutions, and is often the source of endless time-wasting discussions that I really should have stopped engaging in, but sometimes duty calls ;)

Continuing our Fast Failover saga, let’s focus on techniques and technologies available to implement it (assuming you still think it’s worth the effort).

There are numerous technologies you can use to implement fast reroute, from the most complex to the easiest one:

One of my readers encountered an interesting problem when upgrading a data center fabric to 100 Gbps leaf-to-spine links:

• They installed new fiber cables and SFPs;
• Everything looked great… until someone started complaining about application performance problems.
• Nothing else has changed, so the culprit must have been the network upgrade.
• A closer look at monitoring data revealed CRC errors on every leaf switch. Obviously something was badly wrong with the whole batch of SFPs.

Fortunately, my reader took a closer look at the data before they requested a wholesale replacement… and spotted an interesting pattern:

A while ago we had an interesting exchange of ideas around inserting high-availability network appliance into a public cloud environment (TL&DR: it was really hard until AWS introduced Gateway Load Balancing), and someone quickly pointed out we’re solving the wrong challenge because…

Azure Firewall […] is a fully stateful firewall-as-a-service with built-in high-availability.

Somehow he wasn’t too happy when I pointed out that there’s more to high availability than vendor marketing ;)

Remember my BGP route selection rules are a clear failure of intent-based networking paradigm blog post? I wrote it almost three years ago, so maybe you want to start by rereading it…

Making long story short: every large network is a unique snowflake, and every sufficiently convoluted network architect has unique ideas of how BGP route selection should work, resulting in all sorts of crazy extended BGP communities, dozens if not hundreds of nerd knobs, and 2000+ pages of BGP documentation for a recent network operating system (no, unfortunately I’m not joking).