While software design is not the same as network design, there is enough overlap for network designers to learn from software designers. A recent paper published by Butler Lampson, updating a paper he wrote in 1983, is a perfect illustration of this principle. The paper is caleld Hints and Principles for Computer System Design. I’m not going to write a full review here–you should really go read the paper for yourself–but rather just point out some useful bits of the paper.
A recent paper on network control and management (which includes Jennifer Rexford on the author list—anything with Jennifer on the author list is worth reading) proposes a clean slate 4d approach to solving much of the complexity we encounter in modern networks. While the paper is interesting, it’s very unlikely we will ever see a clean slate design like the one described, not least because there will always be differences between what the proper splits are—what should go where.
This week is very busy for me, so rather than writing a single long, post, I’m throwing together some things that have been sitting in my pile to write about for a long while.
From Dalton Sweeny:
A physicist loses half the value of their physics knowledge in just four years whereas an English professor would take over 25 years to lose half the value of the knowledge they had at the beginning of their career. . . Software engineers with a traditional computer science background learn things that never expire with age: data structures, algorithms, compilers, distributed systems, etc. But most of us don’t work with these concepts directly. Abstractions and frameworks are built on top of these well studied ideas so we don’t have to get into the nitty-gritty details on the job (at least most of the time).
The word on the street is that everyone—especially network engineers—must learn to code. A conversation with a friend and an article passing through my RSS reader brought this to mind once again—so once more into the breach. Part of the problem here is that we seem to have a knack for asking the wrong question. When we look at network engineer skill sets, we often think about the ability to configure a protocol or set of features, and then the ability to quickly troubleshoot those protocols or features using a set of commands or techniques.
Have you ever looked at your wide area network and wondered … what would the traffic flows look like if this link or that router failed? Traffic modeling of this kind is widely available in commercial tools, which means it’s been hard to play with these kinds of tools, learn how they work, and understand how they can be effective. There is, however, an open source alternative—pyNTM. While this tool won’t replace a commercial tool, it can give you “enough to go on” for many network operators, and give you the experience and understanding needed to justify springing for a commercial product.
According to Maor Rudick, in a recent post over at Cloud Native, programming is 10% writing code and 90% understanding why it doesn’t work. This expresses the art of deploying network protocols, security, or anything that needs thought about where and how. I’m not just talking about the configuration, either—why was this filter deployed here rather than there? Why was this BGP community used rather than that one? Why was this aggregation range used rather than some other? Even in a fully automated world, the saying holds true.
A short while back, the Linux Foundation (Networking), or LFN, published a white paper about the open source networking ecosystem. Rather than review the paper, or try to find a single theme, I decided to just write down “random thoughts” as I read through it. This is the (rather experimental) result.
For those not following the current state of the ITU, a proposal has been put forward to (pretty much) reorganize the standards body around “New IP.” Don’t be confused by the name—it’s exactly what it sounds like, a proposal for an entirely new set of transport protocols to replace the current IPv4/IPv6/TCP/QUIC/routing protocol stack nearly 100% of the networks in operation today run on. Ignoring, for the moment, the problem of replacing the entire IP infrastructure, what can we learn from this proposal?
I think we can all agree networks have become too complex—and this complexity is a result of the network often becoming the “final dumping ground” of every problem that seems like it might impact more than one system, or everything no-one else can figure out how to solve. It’s rather humorous, in fact, to see a lot of server and application folks sitting around saying “this networking stuff is so complex—let’s design something better and simpler in our bespoke overlay…” and then falling into the same complexity traps as they start facing the real problems of policy and scale.
This complexity cannot be “automated away.” It can be smeared over with intent, but we’re going to find—soon enough—that smearing intent on top of complexity just makes for a dirty kitchen and a sub-standard meal.
Should the network be dumb or smart? Network vendors have recently focused on making the network as smart as possible because there is a definite feeling that dumb networks are quickly becoming a commodity—and it’s hard to see where and how steep profit margins can be maintained in a commodifying market. Software vendors, on the other hand, have been encroaching on the network space by “building in” overlay network capabilities, especially in virtualization products. VMWare and Docker come immediately to mind; both are either able to, or working towards, running on a plain IP fabric, reducing the number of services provided by the network to a minimum level (of course, I’d have a lot more confidence in these overlay systems if they were a lot smarter about routing … but I’ll leave that alone for the moment).
How can this question be answered? One way is to think through what sorts of things need to be done in processing packets, and then think through where it makes most sense to do those things. Another way is to measure the accuracy or speed at which some of these “packet processing things” can be done so you can decide in a more empirical way. The paper I’m looking at today, by Anirudh et al., takes both of these paths in order to create a baseline “rule of thumb” about where to place packet processing functionality in a network.