What is Serverless Computing: Definition, Applications, Advantages, And Best Practices

What is Serverless Computing?

This type of computing is defined as a cloud execution model that provides back-end services on an as-needed basis. The cloud vendor dynamically provides, allocates, and charges the customer for only the resources and storage they use. This way, the customer doesn’t have to pay a fixed rate for a reserved number of servers or bandwidth.

As a side note for those not familiar with the term “back-end,” it’s the part of an application that the user never sees, which of course includes the servers where the user’s database and application files reside.

Even though this process is called “serverless computing,” physical servers are still part of the equation. However, with this computing model, the clients don’t have anything to do with them and don’t need to worry about server maintenance and provisioning. Thus, this frees developers and programmers to focus their time and computing skills on doing their jobs rather than babysit servers.

The term was initially used to describe back-end-as-a-service situations, where customers used a mobile app to connect to a back-end server hosted fully in the cloud. However, the term has gradually evolved to mean a function-as-a-service solution (although FaaS is actually a subset of serverless computing). The client writes business logic code and uploads it to the host provider. The provider, in turn, takes care of virtual machine and container management, hardware provisioning, and related tasks like multithreading.

Since serverless functions are event-driven (e.g., the code activates when requested), the host only charges only for the processing time needed to execute the code.

Benefits and Drawbacks of Serverless Computing

This computing model has attracted a respectable following due to the advantages it brings to the table, though it has its pitfalls too.

Among the advantages of serverless computing:

• It is easily scalable, changing based on actual usage.
• It is cost-effective since you only pay for the processing time you use, rather than throwing money at a fixed amount of time and resource access and potentially not using it all.
• Since developers don’t need to concern themselves with infrastructure, this computing model frees them up to focus on writing code.
• It provides a polyglot environment, meaning developers can code in whatever framework or language they’re most comfortable with (e.g., Python, Java, etc.).
• Some workloads, particularly ones that need parallel processing, run faster (and hence cheaper) using this model.
• Developers can enjoy a quicker turnaround since it potentially reduces time to market significantly. In addition, there is no need to endure a complicated deployment process when rolling out new features and bug fixes; developers can change or add code piecemeal.

No computing process is perfect. Here are the drawbacks associated with serverless computing:

• Serverless environments scale up and down to zero instead of employing long-running processes, so sometimes, they need to start up from zero to work with a new request. This start-up time delay is usually not an issue, but some applications, such as low-latency financial applications, can’t tolerate this lag.
• Not every workload benefits from the scalability of the serverless model. On the other hand, it may not be worth the trouble to go serverless if your organization typically runs steady, predictable, long-running processes. If your organization typically runs steady, predictable, long-running processes, it may not be worth the trouble to go serverless.
• Debugging and monitoring are tough enough to pull off in a distributed system, but the challenge becomes even more significant in a serverless environment.
• Since the vendor controls the whole back-end, developers can’t thoroughly vet the security, proving problematic when applications must access sensitive or personal data.
• If the client wants to avoid vendor lock-in, they may want to reconsider serverless architecture. Many serverless systems are associated with a particular vendor, thus reducing compatibility with other hosts.
• If a developer customarily uses a language that the serverless system doesn’t support, this becomes a non-starter.

Serverless Computing Use Cases and Applications

Serverless computing has many practical uses and applications. Let’s explore a partial list of instances where it fits perfectly.

• Application Programming Interface (API) Back-Ends.

You can turn any function or action on a serverless platform into an HTTP endpoint suitable for web clients. These actions, when enabled for the web, are called web actions. You can assemble web actions into a full-featured API with an API Gateway, bringing additional security and other advantages.

• Data Processing.

It is ideal for working with structured audio, image, text, and video data. You can also use it to work around tasks like cleansing, enriching, transforming, and validating data. Additionally, serverless processing is perfect for audio normalization, image processing, optical character recognition (OCR), PDF processing, and video transcoding.

• Microservices Support.

Serverless computing often supports microservice architectures. Microservices focuses on creating small services that perform a single task and use APIs to communicate. Although microservices can be built and run using containers or Platforms-as-a-Service (PaaS), this model has made remarkable inroads in the process.

• Massive Parallel “Map” and Compute Operations.

Each parallelizable task results in a single action invocation, making serverless computing a good fit. As a result, you can easily perform tasks like Map/reduce operations, data processing and searches, and hyperparameter tuning.

• Everyday Applications.

Increasing numbers of businesses and industries are adopting serverless computing. This chart, supplied courtesy of IBM, breaks down this model into selected applications.

Serverless Frameworks

Serverless frameworks are tools used to deploy functions and events on serverless applications. You can use the frameworks to build mobile, web, and IoT applications within serverless architectures, using AWS Lambda, Azure Functions, Google CloudFunctions, and IBM OpenWhisk.

There is a good selection of serverless frameworks to choose from, including Amazon’s Serverless Application Model (SAM), EcoStruxure, Apex, Claudia.js, Arc.codes, and Serverless. You must admit, that last one’s name pretty much nails it. No room for ambiguity there!

Many of these available serverless frameworks are cross-platform and open-source. The key is finding the framework that works best for you.

Wrapping it Up

Like so many other IT-related innovations, serverless computing isn’t for everyone. Every developer and organization has its needs and special situations. For example, suppose you want an option that is cost-effective, easily scalable, free of maintenance hassles and works well with client-heavy applications and high-latency background tasks such as data processing or multimedia. In that case, you should seriously consider going serverless.

According to this piece on ZDNet.com, serverless computing usage has tripled over the past 12 months. This statistic aligns with an upward trend in serverless adoption rates, from 12 million serverless framework kit downloads in 2019 to 25 million similar downloads in 2020. Companies of all sizes are taking advantage of the benefits of this model from large enterprises down to cloud-native start-ups.

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