CloudWhat is Serverless Computing?

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Serverless Computing Tutorials

If you are in the business of developing software and your team lacks the skills to or resources to maintain a server – or that work becomes a hindrance and interrupts the development process – you may want to consider looking into Serverless computing. Another consideration is that your competitors may be progressing their work and deploying software versions more quickly than your development team. If you are wondering how that is possible, you have come to the right place.

Serverless computing, as its name implies, helps eliminate some of the severe headaches that are associated with the process of developing software and microservices. While developers will still work with servers, those servers will reside in the cloud. Developers and programmers will not interact with the underlying infrastructure of those servers, allowing them to focus their efforts and time on what they love to do: creating software.

In this computing model, the cloud provider will take care of server maintenance and dedicate machine resources and servers on-demand. The pricing of this solution is based on the pay-as-you-go model, which means you only pay for the actual usage of resources. Teams no longer need to worry about any server administration activities, such as configuration, load balancing, maintenance, creating virtual machines (VMs), file systems, or physical servers. Even scaling up and down is performed automatically, as they project requires.

Read: Serverless Functions versus Microservices.

Evolution of Serverless Computing

Serverless computing has four common stages of evolution. They are as follows:

  • Early web applications: In the first stage, you were forced to own your physical underlying infrastructure in order to build a web application, which was costly and stressful.
  • Early cloud computing: In this stage, there was a lot of enhancement, you did not need to use your own servers; instead, you just needed to rent some remote cloud servers, but still, you needed to pay more than you actually used for code executing.
  • Serverless auto-scaling model: In this stage, your payment is set automatically as you utilize resources. However, if you have an issue such as DDoS attacks, you will pay more to cover resources used.
  • Serverless backend services: This means you are paying for only what services you actually use.

Serverless Computing Features

Serverless computing has many features and benefits for coding and software development, including:

  • Offers computing runtimes, known as Function as a Service (FaaS). It executes an application’s code without storing any data; typically supports many common programming languages.
  • Uses isolation boundaries for more security and containers to facilitate testing and packaging applications.
    Applies the “short bursts persisted storage” approach and avoids keeping resources in volatile memory.
  • Meters the usage of on-demand through an event-driven execution model to prevent higher costs when the function is sitting idle and reduces operational costs.
    Divides applications into small, independent parts, optimizing performance.
  • Increases developer productivity and allows for earlier application deployment in the market.
  • Enables DevOps adoption by having many infrastructure options and stronger computing resources.
    Adds the possibility to benefit from third-party Backend as a Service (BaaS) components.

Serverless vs Cloud Computing

If the common factor between Serverless and cloud computing is that there are no physical servers, why consider Serverless as a different service than cloud computing? In some ways, cloud computing is also technically a Serverless service, but Serverless computing defines itself by dispensing with the need to interact with anything related to physical servers and infrastructure. Additionally, the scaling feature which already exists in cloud computing becomes automatic at the Serverless level, so developer teams only need to focus on coding.

With standard cloud computing and Infrastructure-as-a-Service (IaaS), it’s the responsibility of the user to scale up and down the server capacity according to demand. Because of this, the infrastructure stays active even when the user does not run any applications. In the Serverless mode, resources are allocated dynamically for running application code. Additionally, you can also build your apps using a hybrid approach from Serverless and traditional on-premise servers.

When comparing the Platform-as-a-service (PaaS) cloud computing model, you will find it has more similar features to Serverless models – such as the operating systems and middleware as one example. You may also find that PaaS still has some faults, like the difficulty to scale and poorness of edge running, which makes a startup delay and is considered undesirable for development.

Serverless Computing Use Cases and Platforms

There are many use cases for using Serverless computing, including:

  • Processing a batch of image files that rarely display but need to be available nonetheless.
  • Applying a batch of functions when changes occur to a database.
  • Incoming data streams like business logic, hat bots, and scheduled tasks.
  • For some back-end APIs, web apps, and integration across multiple systems.
  • For Knative and Serverless Kubernetes development models.

As for the platforms that offer Serverless computing services, they include the major cloud providers from Amazon (AWS) to Google (GCP) down to Microsoft Azure. Their services include many tasks such as provisioning, auto-scaling, and managing servers.

Some of the most popular Serverless platforms are:

  • AWS Lambda: Provides the user the ability to anonymously run functions that are called Lambda functions. It can also run libraries for executable files from other processes such as Gitlab.
  • Azure Functions: Integrated with all other Azure services and compatible with Microsoft services in your on-premise data center, alongside a strong security level, in addition to supporting CI/CD integration with GitHub.
  • Google Cloud Functions: Empowers users to run code, trigger Serverless functions by events, and deploying open source based on Knative. It supports Node.js, Python, Go, Java, and .NET.

Serverless Security Concerns

Security is the most vulnerable aspect and weak point in the Serverless model. It is well known that many security approaches are useless in the Serverless sphere due to the need to focus on securing servers and static applications.

Luckily, there are some effective practices that can enhance Serverless security, such as:

  • Reducing function permissions into only those necessary to perform a task.
  • Scanning function code for any vulnerabilities or malware, in addition to encrypting keys and secrets.
  • Using security tools for runtime and reducing Serverless functions execution time.
  • Practicing the “One role per Function” approach to prevent functions from getting read access or higher.
  • Being attentive to dependencies to avoid their many vulnerabilities.
  • Using variables for storing configurations such as hostnames and other sensitive information.
  • Automating periodic security checks in CI/CD pipelines for misconfigurations.
  • Considering commercial security products to increase guaranteed and effective protection.

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