…we have educated generations of computer scientists on the paradigm that analysis of algorithm only means analyzing their computational efficiency. As Wikipedia states: “In computer science, the analysis of algorithms is the process of finding the computational complexity of algorithms—the amount of time, storage, or other resources needed to execute them.” In other words, efficiency is the sole concern in the design of algorithms. … What about resilience? —Moshe Y. Vardi
This quote set me to thinking about how efficiency and resilience might interact, or trade off against one another, in networks. The most obvious extreme cases are two routers connected via a single long-haul link and the highly parallel data center fabrics we build today. Obviously adding a second long-haul link would improve resilience—but at what cost in terms of efficiency? Its also obvious highly meshed data center fabrics have plenty of resilience—and yet they still sometimes fail. Why?
One of the difficulties for the average network operator trying to understand their failure rates and reasons is they just don’t have enough devices, or enough incidents, to make informed observations. If you have a couple of dozen switches, it is often hard to understand how often software defects take a device down versus human error (Mean Time Between Mistakes, or MTBM). As networks become larger, however, more information becomes available, and more interesting observations can be made. A recent paper written in conjunction with Facebook uses information from Facebook’s data center fabrics to make some observations about the rate and severity of different kinds of failures—needless to say, the results are fairly interesting.
There is no enterprise, there is no service provider—there are problems, and there are solutions. I’m certain everyone reading this blog, or listening to my podcasts, or listening to a presentation I’ve given, or following along in some live training or book I’ve created, has heard me say this. I’m also certain almost everyone has heard the objections to my argument—that hyperscaler’s problems are not your problems, the technologies and solutions providers user are fundamentally different than what enterprises require.
Let me try to recap some of the arguments I’ve heard used against my assertion.
What is the best way to build a large-scale network—in two words? Ask ten networking folks (engineers, designers, or whatever else), and you’re likely to get the same answer from at least nine: clean abstractions. They might not say the word abstraction, of course; instead, they might say words like build things in modules, using summarization and aggregation to divide the modules up. Or they might say make certain to reduce the failure domain to the smallest you possible can everywhere you can. Or they might say use hierarchical design. These answers are, however, variants of the single word: abstraction.
One of my pet peeves about the network “engineering” world is this: we do too little engineering and too much administration. What brought this to mind this week is an article about Margaret Hamilton about the time she spent working on software development for the Apollo space program, and the lessons she learned about software development there. To wit—
Engineering—back in 1969 as well as here in 2020—carries a whole set of associated values with it, and one of the most important is the necessity of proofing for disaster before human usage. You don’t “fail fast” when building a bridge: You ensure the bridge works first.
Sounds simple in theory—but it is not in practice.
Let’s take, as an example, replacing some of the capacity in your data center designed on a rather traditional two-layer hierarchy, aggregation, and core.
How many 9’s is your network? How about your service provider’s? Now, to ask the not-so-obvious question—why do you care? Does the number of 9’s actually describe the reliability of the network? According to Jeffery Mogul and John Wilkes, nines are not enough. The question is—while this paper was written for commercial relationships and cloud providers, is it something you can apply to running your own network? Let’s dive into the meat of the paper and find out.
While 5 9’s is normally given as a form of Service Level Agreement (SLA), there are two other measures of reliability a network operator needs to consider—the Service Level Objective (SLO), and the Service Level Indicator (SLI).
If you haven’t found the tradeoffs, you haven’t looked hard enough. Something I say rather often—as Eyvonne would say, a “Russism.” Fair enough, and it’s easy enough to say “if you haven’t found the tradeoffs, you haven’t looked hard enough,” but what does it mean, exactly? How do you apply this to the everyday world of designing, deploying, operating, and troubleshooting networks?
Humans tend to extremes in their thoughts. In many cases, we end up considering everything a zero-sum game, where any gain on the part of someone else means an immediate and opposite loss on my part. In others, we end up thinking we are going to get a free lunch. The reality is there is no such thing as a free lunch, and while there are situations that are a zero-sum game, not all situations are. What we need is a way to “cut the middle” to realistically appraise each situation and realistically decide what the tradeoffs might be.
If you are looking for a good resolution for 2020 still (I know, it’s a bit late), you can’t go wrong with this one: this year, I will focus on making the networks and products I work on truly simpler. . . We need to go beyond just figuring out how to make the user interface simpler, more “intent-driven,” automated, or whatever it is. We need to think of the network as a system, rather than as a collection of bits and bobs that we’ve thrown together across the years. We need to think about the modules horizontally and vertically, think about how they interact, understand how each piece works, understand how each abstraction leaks, and be able to ask hard questions.
We normally encounter four different kinds of addresses in an IP network. We tend to assign specific purposes to each one. There are other address-like things, of course, such as the protocol number, a router ID, an MPLS label, etc. But let’s stick to these four for the moment. Looking through this list, the first thing you should notice is we often use the IP address as if it identified a host—which is generally not a good thing. There have been some efforts in the past to split the locator from the identifier, but the IP protocol suite was designed with a separate locator and identifier already: the IP address is the location and the DNS name is the identifier.