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Memory issues shine light on hidden serverless environment costs

This is a guest blog post by Bob Reselman, a nationally known developer, system architect, writer and editor. You can read more of his work at

Serverless computing is all the rage among developers, and with good reason.

A serverless environment is the new vista in modern application development. AWS has Lambda; Microsoft has Azure Functions; Google has Cloud Functions. These technologies are not going away. In fact, we’ll see a lot more work take place to create, build and test code in which the function is the unit of deployment.

Serverless-based applications are easy to architect and easy to deploy. A developer decides the services he needs, wires them up in a script, hits the deploy button and runs some tests — that’s it. Developers don’t need to worry about hardware, capacity or scalability; the serverless provider takes care of all that. Just pay the bill for the resources you use.

It couldn’t be simpler, right? Well, maybe not.

The architecture of a serverless environment with a simple REST API architecture implemented in AWS is fairly straightforward. A set of RESTful endpoints uses Amazon API Gateway and wires each endpoint to some AWS Lambda functions. One Lambda function uses Simple Storage Service (S3) as a data store, and the others store data in an Amazon DynamoDB database.

The API Gateway provides a way to get data in and out of the application; the functions handle computation, while S3 and DynamoDB provide the data storage. What’s not to like? AWS will scale up your application as needed. All you need to do is pay the bill.

So, let’s talk about that bill. Let’s use Will, a systems engineer, as an example.

Will is a low-level engineer who works on content delivery networks for a major telecom. He works closely with bare metal, well below the surface of the average developer’s day-to-day dealings with the cloud. In Will’s world, memory allocation counts.

Over the years, with the growing popularity of higher-level languages such as C# and Java, the common Linux command malloc, which requests memory from the operating system, has become hidden in the language runtime engines, including the common language runtime for .NET and the Java VM. But memory has to be allocated no matter what, and the way you get memory is via the operating system using malloc:

char *str;

str = (char *) malloc(15);

Here is where it interesting: the efficiency of malloc varies depending on your implementation. Standard malloc is inefficient in situations with a high degree of concurrency in multiprocessor environments, so Will won’t use it. It locks up memory — used or unused — and places extra burden on the CPU. Will prefers tcmalloc, created by Google, which exposes configuration capabilities that allow memory allocation to work more efficiently. And it avoids wasteful CPU cycling.

So, what does a memory allocation binary have to do with your AWS bill? It actually has a lot to do with it.

AWS makes money on Lambda by billing you for the time it takes to execute code, which translates into CPU utilization — though you also get billed by your request volume. Thus, every piece of code in your Lambda function that declares a variable is subject to the memory allocation executable, which is most often malloc. That means you might have created code that runs squeaky clean on your local machine or even in a private cloud. But when it gets to AWS, it kills the CPUs.

The provider’s memory allocation infrastructure might not be optimized, so wasteful cycles get spun and you get billed. It’s just like giving a package to a messenger and letting him determine the best route, which might include a lot of stop lights. You pay for the messenger’s time no matter the route efficiency.

Of course, I am not saying AWS is a nefarious agent; quite the opposite. But the serverless environment is theirs to run, and the IT shop doesn’t have a lot say in the matter other than region selection.

Without the ability to optimize a serverless environment to accommodate computationally intensive applications, there is a real financial risk for enterprise IT teams. Hopefully, the major players realize that user optimization for cloud services offers a competitive advantage and more granular capabilities. Otherwise, engineers will fly blind without the aid of instruments on the control panel. And, as we’ve learned on the terrain, when disaster looms, you can’t fix what you can’t see.

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