Landscape With Cloud

September 2020 ยท 6 minute read

Once, in my first tech job, I actually purchased a computer. For maybe eighteen months I’d been responsible for collecting the logs from various programs that ran on a robot, and the Powers That Were had decided that it was worth getting some dedicated hardware. So I spent about a week researching what we needed, figuring out who needed to sign what to get it, and then prodding them until they signed it. Then it took a little while to arrive, and then the sysadmin who needed to install it in the server rack was busy, and then I was busy…all-in-all, it might have taken a month.

My next job, and all my jobs since then, have been on teams that owned almost no actual hardware. That’s pretty much the default, I think. Most software companies use “cloud service providers”–basically, wholesalers of different computing resources–to manage the actual machines on which their programs run. Amazon (AWS) is the biggest of these, followed at a distance by Microsoft and then Google. It’s not a bad state of affairs–these providers spend significant effort trying to make their operations as energy-efficient as possible to keep their costs down.

The early cloud offerings were basically just internet-connected computers that you could rent. You’d get your credentials from the provider’s website, log in, set up whatever you wanted to run (like Wordpress if you wanted a blog) figure out the networking settings, the database, backups if you were ambitious, and the thing would run until doomsday or until the next Wordpress critical vulnerability was found. This was pretty much the state of affairs during my second job–everything was a (remote) computer you could login to. If you wanted a database, you rented a big computer and ran a database on it. If you wanted to make sure your site was always available, you rented a bunch of small computers with fast networking to serve it from. Getting the keys to a big company’s cloud account is kind of like getting a driver’s license– it’s fun at first, but sooner or later it’s just a daily commute.

It also wasn’t very efficient. There are very few useful tasks that require a computer to be doing stuff all the time. Most workloads are spiky– traffic is higher at certain times of day, lower at others. Some workloads need very fast networking, others need lots of storage. Amazon’s smallest instances cost about $0.08 / hour to rent in the mid 2010s. That doesn’t seem like a lot, but it works out to $58 / month for significantly less computing power than a midrange smartphone. And if you were running a personal Wordpress site that got less than a few thousand hits a day, it didn’t make sense. Things weren’t much better for the really big players. There was a lot of reading of tea leaves when it came to capacity planning–do you try to always have enough to cover your biggest spikes? How close can you cut it?

Now, since all of these systems were automated, there were some interesting possibilities. For instance, you could write a program that would watch out for spikes in your traffic and then automatically start more instances to meet the demand, turning them off again when the spike subsided. The cloud vendors integrated these types of controls into their offerings. This put pressure on all kinds of software to become more standard and interchangeable. Where you would once have configured each machine yourself when you started it, now it was important that an automated system could start a computer, get it configured and put it into service without human intervention. One solution to this was the “machine image”– kind of a dehydrated system that could be cloned on demand to start as many identical machines as you wanted.

What I’m trying to illustrate here is that the big idea that has shaped infrastructure development for the past few years is abstraction. We went from computers that were physically taking up space in the office, to computers that were not in our office but which behaved the same as the ones that were, to the blueprint for a particular type of system paired with an algorithm for running that system and scaling it up and down. Each level of abstraction unlocked new efficiencies and made it easier to only pay for what you actually needed to use.

That trend continues, and its latest iteration is described by the buzzword “serverless.” This refers to a system architecture where you do not directly rent any computers at all. Instead, you design your whole system from specialized off-the-shelf parts. Your files are stored in an “object store”– a special system that can store any file up to 5TB, where it costs $0.023 (yes, the prices include fractions of pennies) to store 1GB for a month. For $0.000001 per request, you can set up a forwarding system that will listen for requests or other events and wake up programs to handle them. My test environment probably has around 50 little components organized in neat little groups. My bill for August (not including the online store, which is a different conversation) was a whopping $3.37. I expect September to be even less.

All this is to say that as “means of production” go, some of the very best, most cutting-edge innovations in computing over the last ten years are remarkably inexpensive in money. There are certainly other barriers–and no small amount of bad-faith rent-seeking–that prevent most people from sharing in these benefits. One of those barriers is a lack of awareness and conversation.

On the flip side, these numbers should start to put in perspective just how little one gets in exchange for submitting to constant surveillance by a social network. About one postage-stamp’s worth of computing resources per month, two or three different types of rectangles you’re allowed to use to express yourself, and the creeping anxiety that comes from watching the records of your life slowly sink into the 67483548th length of unsearchable infinite scroll. Anyone who has studied a craft will probably be able to empathize with what it feels like to see such flagrant and tawdry waste in the name of profit.

So it’s a bit of a good-news-bad-news situation. The good news–and it really is pretty unbelievably good, all things considered–is that there’s a plausible way for people without a ton of resources to really make a dent in some of these problems. The bad news is that we’re already in a substantial hole and there are signs of an imminent cave-in.

I’m going to try to continue writing short pieces like this to cover different aspects of the technology landscape, with a focus on things that could be useful for people who would like to imagine a more just world. I hope you’ll come along for the ride and give me the benefit of your insight and experience.