Serverless computing: it can be done without servers!

Serverless computing | ORBIT

How does serverless computing work? What are its advantages, disadvantages and how does it differ from PaaS services?

Martin Gavanda

How would you like to run your applications without worrying about any server infrastructure, be able to respond to performance spikes in seconds (or even less), and pay for it all based on actual usage of individual services? Services like serverless computing make all this possible. No more servers!

Of course, you can't do it without servers, but from the user's point of view, the servers are hidden, you don't pay anything for them, and most importantly, they are you don't have to worry. Serverless computing is a relatively new approach for running certain types of applications, based on the concept of platform as a service (PaaS) takes it one step further in terms of dynamism and flexibility of operation of individual applications.

What is serverless computing

Simply put, serverless computing is about providing certain platform features in the form of pay per use Model. For standard infrastructure as a service (IaaS) or PaaS services you typically pay for a certain "size" of this service usually defined by performance (2 vCPU, 8 GB RAM). you pay for the "volume" of use of the service:

  • how often you run a business function,
  • how many milliseconds of CPU time a single execution of your application consumes,
  • how many messages your apps have exchanged,
  • how many resources your application has used.

In the context of Cloud computing we can therefore encounter the abbreviation FaaS, i.e. Function as a Service.

Source.

Nowadays it is possible to draw various kinds of services in the form of serverless computing:

  • Services for running applications, code or even Kubernetes
  • Database services, both standard relational databases and object
  • Different integration services, notification services, queue processing services or APIs
  • AI and machine learning, text recognition, video analysis or even interactive chat services

Serverless computing can thus be imagined as pool of resources available to me, and every time I use a resource, I pay for it.

Basic differences from PaaS services

Serverless computing and platform as a service are similar in many ways. In both cases no need to worry about the "underlying infrastructure". In both cases, you don't even have to worry about the operating system and middleware. From my point of view, the most important difference is the pay-per-use model and the dynamics of serverless services.

Example 1: Application

Standard PaaS

In the case of using standard PaaS services to run applications (e.g. Azure Web Apps or AWS Elastic Beanstalk) always you pay in a lump sum for the underlying infrastructure size (by default, some equivalent number of CPUs and memory).

You need to know your application, its performance characteristics, and then select the optimal size of the underlying instance based on that. At the same time, you need to take into account the architecture of the application and adapt other follow-on services and technologies (such as Load Balancing), horizontal scaling, and so on.

Serverless computing

If you decide to use serverless computing, you typically use the service Azure Functions or AWS Lambdawhen you pay for the execution (start and execute) a specific function. By function, we mean some clearly defined operation, for example "take an input image, perform an operation on it, report the result to another function and save the image".

It doesn't matter if you run a function four times a month or a thousand times a second. You need to know one thing: what performance is required to perform this function (for example, CPU power or available memory) and you don't care about anything else. The platform automatically provides enough resources to execute the function, and you only pay for the time the function runs (usually billed in milliseconds).

To be even more specific, for example. AWS Lambda is billed on a "per call" basis function (0.2 $ for every 1 million) and allocated "time memory" (for example, 0.0000000333 $ per 1 ms with a 2 GB RAM allocation).

So what does this mean in terms of costs? If you call your feature 5 times per second consistently throughout the calendar month, you will pay 2.59 $. The function takes 50 ms to process. This results in an additional 1.94 $. All in all, roughly 4,5 $ per month. Because AWS Elastic Beanstalk (instance size t2.small) would cost you about 19 $ per month, AWS Lambda is in this particular example roughly 4 times cheaper.

Source : https://aws.amazon.com/lambda/

 

Example 2: Relational database

Standard PaaS

In this case, it is again it is necessary to define the "size" of the instance the database itself, and this performance is assigned to the customer. If your application doesn't use it, it's a wasted investment. Conversely, if a situation arises where the application needs more performance, it's not exactly easy to react to that situation and "scale" the database on the fly. Yes, the options are there, but it's not trivial and your application needs to be prepared for it.

Serverless computing

If your application has variable performance requirements that change dramatically over time, it may be ideal to use a serverless database, such as Azure SQL Database or AWS Aurora. Be sure to study the documentation of each clamp in detail, because the serverless option is not available for every type of database engine or for a specific configuration.

For serverless database again you pay for the allocation of a specific performance over time. For example, for Azure SQL Database in serverless mode, you pay €0.0001345 per vCore (processing unit) per second of usage.

Of course, you can define the minimum and maximum performance values required, and you can even specify whether the database service can be temporarily suspended when it is not in use. Your database will then dynamically allocate a sufficient number of vCores so that you are able to achieve optimal performance.

Source : https://docs.microsoft.com/cs-cz/azure/azure-sql/database/serverless-tier-overview

Let's compare costs again. Two vCores provided in PaaS "provisioned mode" cost in Azure SQL approximately EUR 356 per month. In FaaS mode, the same database costs 0.5218 $/vCore-hour. If we use it during working hours at an average of 1 vCore, we will probably receive an invoice for just over 100 EUR while maintaining the same performance parameters.

Not all gold glitters

As is often the case with technology, there will always be advantages and disadvantages, so it is important to consider, when a particular technology is suitable for you. This is also true for serverless computing.

From my point of view, I perceive three main disadvantages of serverless computing:

  • I have to detail know your application and its performance requirementswhich may not always be easy to determine. If my application doesn't have performance spikes and is loaded at a constant rate, serverless computing is likely to be more expensive than standard PaaS or IaaS services.
  • In the form of serverless computing it is not possible to run all applications, for example, is not suitable for monolithic Java applications.
  • I need to know all the nuances of each serverless service, because as they say, "the devil is in the details". Hand in hand with flexibility always comes a certain platform boundedness. Whether these are different "minima-maxima" or limitations of a specific technical implementation.

What applications should I use serverless computing for?

Because serverless computing is a relatively new form of service delivery, it is suitable primarily for newly developed applications. There are certainly scenarios where complex refactoring of an existing application (especially applications with highly variable performance requirements) can make economic sense.

In general, I would use serverless computing primarily for newly developed applications, where with the right implementation you can achieve de facto unlimited possibilities in terms of scaling and performance at a very reasonable price.

Sample diagram of a complex application using pure serverless computing (source: https://docs.microsoft.com/en-us/azure/architecture/example-scenario/ai/news-feed-ingestion-and-near-real-time-analysis)

This approach is also linked to another concept of application architecture, the so-called event-driven approach. Previously, apps "just ran" and were "available to anyone at any time". Event-driven architecture is based on the assumption that "reacting to an event".

So we have typically producers these events (something happened; user uploaded an image, business transaction was triggered, account status changed, new order arrived).

Then there are components event routerswhich are responsible for routing messages to individual customers (who should know about this; if there is a new image, this and that needs to be done, the new order needs to be processed by these follow-up applications).

The last link in the chain is the processors (consumers). They are responsible for performing a specific action based on a specific event (what is to happen; if a new image has arrived, certain operations need to be performed, a new order means its processing and entry into the ERP system).

Sample of event-driven architecture (source: https://aws.amazon.com/blogs/compute/operating-lambda-understanding-event-driven-architecture-part-1/)

Serverless computing: yes or no?

Serverless computing is not suitable for every software implementation, but the more my applications are "cloud native" and developed for the cloud, the more potential benefits serverless computing offers me.

With the right use of serverless computing, my application can be much more robust, cheaper and more accessible than traditional PaaS services.

So how do you decide which applications to use which service and technology for? This is what my colleague will focus on Lukas Hudecek in his upcoming paper on application analysis.

About the author
Martin Gavanda
Martin Gavanda

Cloud Architect | LinkedIn

Martin is a senior consultant focusing mainly on public cloud - Amazon Web Services and Microsoft Azure.

Technical knowledge: Public Clouds (Azure & AWS), Cloud Architecture and Design, Cloud Security, Kubernetes & Cloud Native application design, Application Assessments & Migrations.