Software Eats the World?
I’m told software is going to eat the world very soon now. Everything already is, or will be, software based. To some folks, this sounds completely wonderful, but—leaving aside the privacy issues—I still see an elephant in the room with this vision of the future.
Let me give you some recent examples.
First, ceiling fans. Modern ceiling fans, in case you didn’t know, don’t rely on the wall switch and pull chains. Instead, they rely on remote controls. This is brilliant—you can dim the light, change the speed of the fan, etc., from a remote control. No unsightly chains hanging from the ceiling.
Well, it’s brilliant so long as it works. I’ve replaced three of the four ceiling fans in my house. Two of the remote controls have somehow attached themselves to two of the three fans. It’s impossible to control one of the fans without also controlling the other. They sometimes get into this entertaining mode where turning one fan off turns the other one on.
For the third one—the one hanging from a 13-foot ceiling—the remote control sometimes operates one of the other fans, and sometimes the fan its supposed to operate. Most of the time it doesn’t seem to do much of anything.
The fan manufacturer—a large, well-known company—mentions this situation in their instructions and points to a FAQ that doesn’t exist. Searching around online I found instructions for solving this problem that involve unwiring the fans and repeating a set of steps 12 times for each fan to correct the situation. These instructions, needless to say, don’t work.
There is no way to reset the remote, nor the connection between the remote and the fan. There is no way to manually select some dip switch so the remote has a specific fan it talks to. Just some mystical software that’s supposed to work (but doesn’t) and no real instructions on how to resolve the problem. The result will be a multi-hour wait on a customer support line, spending hours of my time to sort the problem out, and the joy of climbing (tall) ladders to unwire and wire ceiling fans in four different rooms.
Thinking through possible problems and building software interfaces that take those situations into account … might be a bit more important than we think they are if software is really going to eat the world.
Second, the retailer’s web site—a large retailer with thousands of physical stores across the United States. Twice I’ve ordered from this site, asking to have the item held in the local store so I can pick it up. The site won’t let you order the item for store pickup unless they have it in stock.
The first time they called me to say they couldn’t find the item I ordered, but they found a “newer model” that was a lot less expensive. It was a lot less expensive because it wasn’t the same item. They never did find the item I originally ordered.
The second time they called me to say they couldn’t find the item I ordered. I asked if they could just ship the item to my house when it’s back in stock. “I’m sorry, our system doesn’t allow us to do that …” Several hours later, they called back to tell me they found it, but they cannot reinstate my order—I must place a new order.
Again, software quality strikes … what should be a simple process just isn’t. There will always be mismatches between the state in software and the state in the real world—but design the system so it’s possible to adapt when this happens, rather than shutting down the process and starting over.
Third, I own a car that has all the “bells and whistles,” including an adaptive cruise control system. There are certain situations, however, where this adaptive control does the wrong thing, producing potentially dangerous results. There is no way to set the car to use the non-adaptive cruise control permanently (I called and waited on the phone for several hours to discover this). You can set the non-adaptive cruise control on a per-use basis by going through set of menus to change the settings … while driving.
Software quality anyone?
Software eats the world might be someone’s ultimate dream—but I suspect that software quality will always be the fly in the ointment. People are not perfect (even in crowds); software is created by people; hence software will always suffer from quality problems.
Maybe a little humility about our ability to make things as complex as we might like because “we can always have software do that bit” would be a good thing—even in the networking world.
Way in the past, the EIGRP team (including me) had an interesting idea–why not aggregate routes automatically as much as possible, along classless bounds, and then deaggregate routes when we could detect some failure was causing a routing black hole? To understand this concept better, consider the network below.
In this network, B and C are connected to four different routers, each of which is advertising a different subnet. In turn, B and C are aggregating these four routes into 2001:db8:3e8:10::/60, and advertising this aggregate towards A. From a control plane state perspective, this is a major win. The obvious gain is that the amount of state is reduced from four routes to one. The less obvious gain is A doesn’t need to know about any changes in the state for the four destinations aggregated into the /60. Depending on how often these links change state, the reduction in the rate of change is, perhaps, more important than the reduction in the amount of control plane state.
We always know there will be a tradeoff when reducing state; what is the tradeoff here? If C somehow loses its connection to one of the four routers, say the router advertising 11::/64, C’s 10::/60 aggregate will not change. Since A thinks C still has a route to every subnet within 10::/60, it will continue sending traffic destined to addresses in the 11::/64 towards both B and C. C will not have a route towards these destinations, so it will drop the traffic.
We have a routing black hole.
This much is pretty simple. The harder part is figuring out to eliminate this routing black hole. Our first choice is to just not aggregate these routes. While you might be cringing right now, this isn’t such a bad option in many networks. We often underestimate the amount of state and the speed of state change modern routing protocols running on modern processors can support. I’ve seen networks running IS-IS in a single flooding domain with tens of thousands of routes and thousands of nodes running “in the wild.” I’ve seen IS-IS networks with thousands of nodes and hundreds of thousands of routes running in lab environments. These networks still converge.
But what if we really think we need to reduce the amount and speed of state, so we really need to aggregate these routes?
One solution that has been proposed a number of times through the years is auto disaggregation.
In this case, suppose D somehow realizes C cannot reach one of the components of a shared aggregate route. D could simply stop advertising the aggregate, advertising each of the components instead. The question here might be: is this a good idea? Looking at this from the perspective of the SOS triad, the aggregation replaced four routes with a single route. In the auto disaggregation case, the single route change is replaced by four route changes. The amount of state is variable, and in some cases the rate of change in state is actually higher than without the aggregation.
I don’t hold that auto disaggregation is either good nor bad—it just presents a different set of challenges to the network designer. Instead of designing for average rates of change and given table sizes, you can count on much smaller tables, but you might find there are times when the rate of change is dramatically higher than you expect. A good question to ask, before deploying this kind of technology, might be: can I forsee a chain of events that will cause a high enough rate of state change that auto disaggregation is actually more destabilizing than just not summarizing at all in this network?
A real danger with auto disaggregation, by the way, is using summarization to dramatically reduce table sizes without understanding how a goldilocks failure (what we used to call in telco a mother’s day event, or perhaps a black swan) can cascade into widespread failures. If you’re counting on particular devices in your network only have a dozen or two dozen table entries, but just the right set of failures can cause them to have several thousand entries because of auto disaggregation, what kinds of failures modes should you anticipate? Can you anticipate or mitigate this kind of problem?
The idea of automatically summarizing and disaggregating routes is an interesting study in complexity, state, and optimization. It’s a good brain exercise in thinking through what-if situations, and carefully thinking about when and where to deploy this kind of thing.
What do you think about this idea? When would you deploy it, where, and why? When and where would you be cautious about deploying this kind of technology?
I sometimes reference Keith’s Law in my teaching, but I don’t think I’ve ever explained it. Keith’s Law runs something like this:
Any large external step in a system’s capability is the result of many incremental changes within the system.
The reason incremental changes within a system appear as a single large step to outside observers is the smaller changes are normally hidden by abstraction. This is, in fact, the purpose of abstraction—to hide small changes inside a system from external view. Keith’s law is closely related to Clarke’s third law that “Any sufficiently advanced technology is indistinguishable from magic.” What looks like magic from the outside is really just a bunch of smaller things—each easier to understand on its own—combined into one single “thing” through abstraction.
If you’ve read this far, you’re probably thinking—what does this have to do with network engineering?
Well, several things, really.
First—the network is just an abstraction that moves packets to its users. Moving packets seems so … simple … to network users. You put data in here, and data comes out over there. All the little stuff that goes into making a network work are lost in the abstraction of the virtual connection between two hosts.
If you want users to understand why building a network is hard, you’re going to have to work hard at it. And you’re not likely to succeed—it’s often better just to live with the reality that users aren’t going to understand. Of course, this isn’t necessarily a bad thing, at least until it’s time to buy hardware and software to make all this magic work.
Second—no-one outside the network is ever going to understand the refactoring, simplification, and new features you’re trying to build into the network on their own. Users will only understand these things when they are related to some bigger picture, something they can see beyond the abstraction the network presents.
If you’re going to justify doing new things, you need to do so in terms of “larger things,” things that can be seen from outside the abstraction.
Third—no-one is going to pat you on the back for all the little things that need to be done to deploy a new major service. From the outside, that new service, or new cost savings, or whatever—it’s all just indistinguishable from magic.
Keith’s law is both good and bad. But it also means you need to learn how to frame your work in a way that users, who don’t have access to the inner workings of the network, can understand why you’re doing what you’re doing.
Turning this around, this also means you shouldn’t accept the “magic” of vendor products. That brilliant new capability your vendor is showing you is really made up of a lot of smaller components. The abstraction is just that—an abstraction. If you really want to understand the positive and negative consequences of deploying something new, you need to look beyond the abstraction.
One of the designs I’ve been encountering a lot of recently is a “collapsed spine” data center network, as shown in the illustration below.
In this design, and B are spine routers, while C-F are top of rack switches. The terminology is important here, because C-F are just switches—they don’t route packets. When G sends a packet to H, the packet is switched by C to A, which then routes the packet towards F, which then switches the packet towards H. C and F do not perform an IP lookup, just a MAC address lookup. A and B are responsible for setting the correct next hop MAC address to forward packets through F to H.
What are the positive aspects of this design? Primarily that all processing is handled on the two spine routers—the top of rack switches don’t need to keep any sort of routing table, nor do any IP lookups. This means you can use very inexpensive devices for your ToR. In brownfield deployments, so long as the existing ToR devices can switch based on MAC addresses, existing hardware can be used.
This design also centralizes almost all aspects of network configuration and management on the spine routers. There is little (if anything) configured on the ToR devices.
What about negative aspects? After all, if you haven’t found the tradeoffs, you haven’t looked hard enough. What are they here?
First, I’m struggling to call this a “fabric” at all—it’s more of a mash-up between a traditional two-layer hierarchical design with a routed core and switched access. Two of the points behind a fabric are the fabric doesn’t have any intelligence (all ports are undifferentiated Ethernet) and all the devices in the fabric are the same.
I suppose you could say the topology itself makes it more “fabric-like” than “network-like,” but we’re squinting a bit either way.
The second downside of this design is that it impacts the scaling properties of the fabric. This design assumes you’ll have larger/more intelligent devices in the spine, and smaller/less intelligent devices in the ToR. One of my consistent goals in designing fabrics has always been to push as close to single-sku as possible—use the same device in every position in the fabric. This greatly simplifies instrumentation, troubleshooting, and supply chain management.
One of the primary points of moving from a network in the more traditional sense to a “true fabric” is to radically simplify the network—this design doesn’t seem like it’s as “simple,” on the network side of things, as it could be. Again, something of a “mash-up” of a simpler fabric and a more traditional two-layer hierarchical routed/switched network.
Scale-out is problematic in this design, as well. You’d need to continue pushing cheap/low-intelligence switches along the edge, and adding larger devices in the spine to make this work over time. At some point, say when you have eight or sixteen spines, you’d be managing just as much configuration—and configuration that’s necessarily more complex because you’re essentially managing remote ports rather than local ones—as you would by just moving routing down to the ToR devices. There’s some scale point here with this design where it’s adding overhead and unnecessary complexity to save a bit of money on ToR switches.
When making the choice between OPEX and CAPEX, we should all know which one to pick.
Where would I use this kind of design? Probably in a smaller network (small enough not to use chassis devices in the spine) which will never need to be scaled out. I might use it as a transition mechanism to a full fabric at some point in the future, but I would want a well-designed planned to transition—and I would want it written in stone that this would not be scaled in the future beyond a specific point.
There’s nothing more permanent in the world than temporary government programs and temporary network designs.
If anyone has other thoughts on this design, please leave them in the comments below.
We have the twelve truths of networking, and possibly Akin’s Laws, but is there a set of rules for network design? I couldn’t find one, so I decided to create one, containing 18 laws I’ve listed below.
Russ’ Rules of Network Design
- If you haven’t found the tradeoffs, you haven’t looked hard enough.
- Design is an iterative process. You probably need one more iteration than you’ve done to get it right.
- A design isn’t finished when everything needed is added, it’s finished when everything possible is taken away.
- Good design isn’t making it work, it’s making it fail gracefully.
- Effective, elegant, efficient. All other orders are incorrect.
- Don’t fix blame; fix problems.
- Local and global optimization are mutually exclusive.
- Reducing state always reduces optimization someplace.
- Reducing state always creates interaction surfaces; shallow and narrow interaction surfaces are better than deep and broad ones.
- The easiest place to improve or screw up a design is at the interaction surfaces.
- The optimum is almost always in the middle someplace; eschew extremes.
- Sometimes its just better to start over.
- There are a handful of right solutions; there is an infinite array of wrong ones.
- You are not immensely smarter than anyone else in networking.
- A bad design with a good presentation is doomed eventually; a good design with a bad presentation is doomed immediately.
- You can only know your part of the system and a little bit about the parts around your part. The rest is rumor and pop psychology.
- To most questions the correct initial answer should be “how many balloons fit in a bag?”
- Virtual environments still have hard physical limits.
Off-topic post for today …
In the battle between marketing and security, marketing always wins. This topic came to mind after reading an article on using email aliases to control your email—
For example, if you sign up for a lot of email newsletters, consider doing so with an alias. That way, you can quickly filter the incoming messages sent to that alias—these are probably low-priority, so you can have your provider automatically apply specific labels, mark them as read, or delete them immediately.
One of the most basic things you can do to increase your security against phishing attacks is to have two email addresses, one you give to financial institutions and another one you give to “everyone else.” It would be nice to have a third for newsletters and marketing, but this won’t work in the real world. Why?
Because it’s very rare to find a company that will keep two email addresses on file for you, one for “business” and another for “marketing.” To give specific examples—my mortgage company sends me both marketing messages in the form of a “newsletter” as well as information about mortgage activity. They only keep one email address on file, though, so they both go to a single email address.
A second example—even worse in my opinion—is PayPal. Whenever you buy something using PayPal, the vendor gets the email address associated with the account. That’s fine—they need to send me updates on the progress of the item I ordered, etc. But they also use this email address to send me newsletters … and PayPal sends any information about account activity to the same email address.
Because of the way these things are structured, I cannot separate information about my account from newsletters, phishing attacks, etc. Since modern Phishing campaigns are using AI to create the most realistic emails possible, and most folks can’t spot a Phish anyway, you’d think banks and financial companies would want to give their users the largest selection of tools to fight against scams.
But they don’t. Why?
Because—if your financial information is mingled with a marketing newsletter, you’ll open the email to see what’s inside … you’ll pay attention. Why spend money helping your users not pay attention to your marketing materials by separating them from “the important stuff?”
When it comes to marketing versus security, marketing always wins. Somehow, we in IT need to do better than this.
Everyone is aware that it always takes longer to find a problem in a network than it should. Moving through the troubleshooting process often feels like swimming in molasses—you’re pulling hard, and progress is being made, but never fast enough or far enough to get the application back up and running before that crucial deadline. The “swimming in molasses effect” doesn’t end when the problem is found out, either—repairing the problem requires juggling a thousand variables, most of which are unknown, combined with the wit and sagacity of a soothsayer to work with vendors, code releases, and unintended consequences.
It’s enough to make a network engineer want to find a mountain top and assume an all-knowing pose—even if they don’t know anything at all.
The problem of taking longer, though, applies in every area of computer networking. It takes too long for the packet to get there, it takes to long for the routing protocol to converge, it takes too long to support a new application or server. It takes so long to create and validate a network design change that the hardware, software and processes created are obsolete before they are used.
Why does it always take too long? A short story often told to me by my Grandfather—a farmer—might help.
One morning a farmer got early in the morning, determined to throw some hay down to the horses in the stable. While getting dressed, he noticed one of the buttons on his shirt was loose. “No time for that now,” he thought, “I’ll deal with it later.” Getting out to the barn, he climbed up the ladder to the loft, and picked up a pitchfork. When he drove the fork into the hay, the handle broke.
He sighed, took the broken pieces down the ladder, and headed over to his shed to replace the handle—but when he got there, he realized he didn’t have a new handle that would fit. Sighing again, he took the broken pieces to his old trusty truck and headed into town—arriving before the hardware store opened. “Well, I’m already here, might as well get some coffee,” he thought, so he headed to the diner. After a bit, he headed to the store to buy a handle—but just as he walked out past the door, the loose button caught on the handle, popping off.
It took a few minutes to search for the lost button, but he found it and headed over to the cleaners to have it sewn back on “real fast.” Well, he couldn’t wander around town in his undershirt, so he just stepped next door to the barber’s, where there were a few friendly games of checkers already in progress. He played a couple of games, then the barber came out to remind him that he needed a haircut (a thing barbers tend to do all the time for some reason), so he decided to have it done. “Might was well not waste the time in town now I’m here,” he thought.
The haircut finished, he went back to get his shirt, and realized it was just about lunch. Back to the diner again. Once he was done, he jumped in his truck and headed back to the farm. And then he realized—the horses were hungry, the hay hadn’t been pitched, and … his pitchfork was broken.
And this is why it always takes longer than it should to get anything done with a network. You take the call and listen to the customer talk about what the application is doing, which takes a half an hour. You then think about what might be wrong, perhaps kicking a few routers “just for good measure” before you start troubleshooting in earnest. You look for a piece of information you need to understand the problem, only to find the telemetry system doesn’t collect that data “yet”—so you either open a ticket (a process that takes a half an hour), or you “fix it now” (which takes several hours). Once you have that information, you form a theory, so you telnet into a network device to check on a few things… only to discover the device you’re looking at has the wrong version of code… This requires a maintenance window to fix, so you put in a request…
Once you even figure out what the problem is, you encounter a series of hurdles lined up in front of you. The code needs to be upgraded, but you have to contact the vendor to make certain the new code supports all the “stuff” you need. The configuration has to be changed, but you have to determine if the change will impact all the other applications on the network. You have to juggle a seemingly infinite number of unintended consequences in a complex maze of software and hardware and people and processes.
And you wonder, the entire time, why you just didn’t learn to code and become a backend developer, or perhaps a mountain-top guru.
So the next time you think it’s taking to long to fix the problem, or design a new addition to the network, or for the vendor to create that perfect bit of code, remember the farmer, and the button that left the horses hungry.