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.
This last week I was talking to someone at a small startup that intends to eliminate all the complex routing from campus networks. In the past, when reading blog posts about Kubernetes, I’ve read about how it was designed to eliminate routing protocols because “routing protocols are so complex.”
Color me skeptical.
There are two reasons for complexity in a design. The first is you’re solving a hard problem. The second is you’ve made bad design choices in the past, and you’re pasting complexity on top to solve some perceived problem (whether perceived or real).
The problem with all this talk about building something that’s “less complex” is people tend to see complexity of the first kind and think, “we can get rid of that complexity if we start over.” Failing to understand the past before building the future is a recipe for repeated failures of the same kind. Building a network without a distributed routing protocol hasn’t been tried before either, right? Well, yes, it has … We either forget how it turned out, or we say “well, that’s not the same thing I’m talking about here” (just like “real socialism hasn’t ever been tried”).
Even worse, they think they get rid of second and third kinds of complexity by starting over, or getting the humans out of the decision-making loop, or focusing on the data. Our modern penchant for relying “the data,” without ever thinking about the source of the data or how the data has been shaped and interpreted, is truly breathtaking.
They look over the horizon, see an unspoiled field, and think “the grass really is greener on the other side.”
Get rid of all those complex dynamic routing protocols … get rid of all those humans making decisions, so the decisions are “data driven” … and everything will be so much better.
Adding complexity to solve hard real-world problems is just the way things are, and they will always be, so the first reason for complexity will always be with us. People make mistakes, don’t see into the future perfectly, or just don’t have a perfect understanding of the system (technical debt), so the second kind of complexity will always be with us. You can’t “fix” people—God save us from those who think they can. The grass isn’t always greener—it just always looks that way.
What’s the practical upshot? Networks are always going to be complex. It’s just the nature of the problem being solved.
We add complexity because we fail to ask the right questions, we don’t understand the system, or we fail to do good design. The solution isn’t to seek out a greener field “out there,” but rather to make the field we currently live in greener by asking the right questions and reducing complexity through good design. Sometimes you might even need to start over with a new network … but when you start thinking about starting over with a newly designed set of protocols because the old ones are “too complex,” you need to ask how those old ones got that way, and how you’re going to stop the new ones from getting to the same place.
The grass is always greener because you looking at it through green-colored lenses just as the new grass is in its full flush, and before the weeds have had a chance to take over.
Learn how old things worked before you fall for some new “modern wonder” that’s going to solve every problem. The complexity in old things will show you where you can expect to find complexity grow up in new things.
Fear sells. Fear of missing out, fear of being an imposter, fear of crime, fear of injury, fear of sickness … we can all think of times when people we know (or worse, a people in the throes of madness of crowds) have made really bad decisions because they were afraid of something. Bruce Schneier has documented this a number of times. For instance: “it’s smart politics to exaggerate terrorist threats” and “fear makes people deferential, docile, and distrustful, and both politicians and marketers have learned to take advantage of this.” Here is a paper comparing the risk of death in a bathtub to death because of a terrorist attack—bathtubs win.
But while fear sells, the desire to appear unafraid also sells—and it conditions people’s behavior much more than we might think. For instance, we often say of surveillance “if you have done nothing wrong, you have nothing to hide”—a bit of meaningless bravado. What does this latter attitude—“I don’t have anything to worry about”—cause in terms of security?
Several attempts at researching this phenomenon have come to the same conclusion: average users will often intentionally not use things they see someone they perceive as paranoid using. According to this body of research, people will not use password managers because using one is perceived as being paranoid in some way. Theoretically, this effect is caused by illusory correlation, where people associate an action with a kind of person (only bad/scared people would want to carry a weapon). Since we don’t want to be the kind of person we associate with that action, we avoid the action—even though it might make sense.
This is just the flip side of fear sells, of course. Just like we overestimate the possibility of a terrorist attack impacting our lives in a direct, personal way, we also underestimate the possibility of more mundane things, like drowning in a tub, because we either think can control it, or because we don’t think we’ll be targeted in that way, or because we want to signal to the world that we “aren’t one of those people.”
Even knowing this is true, however, how can we counter this? How can we convince people to learn to assess risks rationally, rather than emotionally? How can we convince people that the perception of control should not impact your assessment of personal security or safety?
Simplifying design and use of the systems we build would be one—perhaps not-so-obvious—step we can take. The more security is just “automatic,” the more users will become accustomed to deploying security in their everyday lives. Another thing we might be able to do is stop trying to scare people into using these technologies.
In the meantime, just be aware that if you’re an engineer, your use of a technology “as an example” to others can backfire, causing people to not want to use those technologies.
I cannot count the number of times I’ve heard someone ask these two questions—
- What are other people doing?
- What is the best common practice?
While these questions have always bothered me, I could never really put my finger on why. I ran across a journal article recently that helped me understand a bit better. The root of the problem is this—what does best common mean, and how can following the best common produce a set of actions you can be confident will solve your problem?
Bellman and Oorschot say best common practice can mean this is widely implemented. The thinking seems to run something like this: the crowd’s collective wisdom will probably be better than my thinking… more sets of eyes will make for wiser or better decisions. Anyone who has studied the madness of crowds will immediately recognize the folly of this kind of state. Just because a lot of people agree it’s a good idea to jump off a cliff does not mean it is, in fact, a good idea to jump off a cliff.
Perhaps it means something closer to this is no worse than our competitors. If that’s the meaning, though, it’s a pretty cynical result. It’s saying, “I don’t mind condemning myself to mediocrity so long as I see everyone else doing the same thing.” It doesn’t sound like much of a way to grow a business.
The authors do provide their definition—
For a given desired outcome, a “best practice” is a means intended to achieve that outcome, and that is considered to be at least as “good” as the best of other broadly considered means to achieve that same outcome.
The thinking seems to run something like this—it’s likely that everyone else has tried many different ways of doing this; that they have all settled on doing this, this way, means all those other methods are probably not as good as this one for some reason.
Does this work? There’s no way to tell without further investigation. How many of the other folks doing “this” spent serious time trying alternatives, and how many just decided the cheapest way was the best no matter how poor the result might be? In fact, how can we know what the results of doing things “this way” have in all those other networks? Where would we find this kind of information?
In the end, I can’t ever make much sense out of the question, “what is everyone else doing?” Discovering what everyone else is doing might help me eliminate possibilities (that didn’t work for them, so I certainly don’t want to try it), or it might help me understand the positive and negative attributes of a given solution. Still, I don’t understand why “common” should infer “best.”
The best solution for this situation is simply going to be the best solution. Feel free to draw on many sources, but don’t let other people determine what you should be doing.
Recent research into the text of RFCs versus the security of the protocols described came to this conclusion—
This should come as no surprise to network engineers—after all, complexity is the enemy of security. Beyond the novel ways the authors use to understand the shape of the world of RFCs (you should really read the paper; it’s really interesting), this desire to increase security by decreasing the ambiguity of specifications is fascinating. We often think that writing better specifications requires having better requirements, but down this path only lies despair.
Better requirements are the one thing a network engineer can never really hope for.
It’s not just that networks are often used as a sort of “complexity sink,” the place where every hard problem goes to be solved. It’s also the uncertainty of the environment in which the network must operate. What new application will be stuffed on top of the network this week? Will anyone tell the network folks about this new application, or just open a ticket when it doesn’t work right? What about all the changes developers are making to applications right now, and their impact on the network? There are link failures, software failures, hardware failures, and the mean time between mistakes. There is the pace of innovation (which I tend to think is a bit overblown—rule11, after all—we are often talking about new products rather than new ideas).
What the network is supposed to do—just provide IP transport between two devices—turns out to be hard. It’s hard because “just transporting packets” isn’t ever enough. These packets must be delivered consistently (jitter and drops) across an ever-changing landscape.
To this end—
[C]omplexity is most succinctly discussed in terms of functionality and its robustness. Specifically, we argue that complexity in highly organized systems arises primarily from design strategies intended to create robustness to uncertainty in their environments and component parts.
Uncertainty is the key word here. What can we do about all of this?
We can reduce uncertainty. There are three ways to reduce uncertainty. First, you can obfuscate it—this is harmful. Second, you can reduce the scope of the job at hand, throwing some of the uncertainty (and therefore complexity) over the cubicle way. This can be useful in some situations, but remember that the less work you’re doing, the less value you add. Beware of self-commodifying.
Finally, you can manage the uncertainty. This generally means using modularization intelligently to partition off problems into smaller sets. It’s easier to solve a set of well-scope problems with little uncertainty than to solve one big problem with unknowable uncertainty.
This might all sound great in theory, but how do we do this in real life? Where does the rubber hit the road? This is what Ethan and I tried to show in Problems and Solutions—how to understand the problems that need to be solved, and then how to solve each of those problems within a larger system. This is also what many parts of The Art of Network Architecture are about, and then again what Jeff and I wrote about in Navigating Network Complexity.
I know it often seems like it’s not worth learning the theory; it’s so much easier to focus on the day-to-day, the configuration of this device, or the shiny thing that vendor just created. It’s easier to assume that if I can just hide all the complexity behind intent or automation, I can get my weekends back.
The truth is that we’re paid to solve hard problems, and solving hard problems involves complexity. We can either try to cover that up, or we can learn to manage it.
Decision making, especially in large organizations, fails in many interesting ways. Understanding these failure modes can help us cope with seemingly difficult situations, and learn how to make decisions better. On this episode of the Hedge, Federico Lucifredi, Ethan Banks, and Russ White discuss Federico’s thoughts on developing a taxonomy of indecision. You can find his presentation on this topic here.
Crossing from the domain of test pilots to the domain of network engineering might seem like a large leap indeed—but user interfaces and their tradeoffs are common across physical and virtual spaces. Brian Keys, Eyvonne Sharp, Tom Ammon, and Russ White as we start with user interfaces and move into a wider discussion around attitudes and beliefs in the network engineering world.