Improving DevOps with Serverless Computing

If you want your teams to focus on front-end development and services, consider serverless computing. Serverless computing refers to outsourcing IT infrastructure to external providers.

It features flexible use of resources that can be scaled based on real-time requirements. As a result, it is preferred by DevOps due to its faster development lifecycle.

How does serverless computing work?

Serverless computing provides provisioning, scheduling, scaling, and other back-end cloud infrastructure and operations tasks to the cloud provider. This gives developers more time to develop front-end applications and business logic. This relieves the workload of your teams and ensures that their maximum focus is on innovation.

While it does technically use servers, it is called serverless computing because in this case the servers are hosted by a third-party service provider, making them seemingly non-existent for the customer, who is not responsible for managing them. This is an essential step towards NoOps (no operations).

Every major cloud service provider offers a serverless platform, including Microsoft Azure, Google Cloud, and Amazon Web Services. Serverless is also at the heart of cloud-native application development.

Also read: Is serverless computing ready to go mainstream?

How do serverless platforms improve DevOps?

The serverless model is more suitable for certain customers than the IaaS and SaaS models, which charge a fixed monthly or annual price. Sometimes developers don’t need to use the full capacity of their cloud solution.

In such cases, serverless computing provides a fine-grained, pay-as-you-go model so that you only pay for the resources consumed for the lifetime of the called function. This can lead to significant reductions in projected costs, enabling greater savings over other cloud models for many workloads.

However, serverless models can only be considered an evolving technology at best. Considering it a universal solution to development and operational problems, this model can lead to certain drawbacks.

That said, IT professionals have reported using serverless for a wide variety of applications, including customer relationship management (CRM), finance, and business intelligence.

Popular uses of serverless computing

Many major cloud service providers offer serverless platforms to users who can finally enjoy NoOps status, including Amazon, Google, and Microsoft. Serverless platforms from Alibaba, IBM and Oracle, among others, will follow shortly. At the same time, open source projects such as OpenFaaS (function as a service) and Kubeless are bringing serverless technologies to the on-premises architecture.

Get support for microservice architecture

An important use of serverless computing is to support microservice architectures, which allow the creation of small services with a single task that APIs can use to connect with each other.

Serverless is ideally suited for this model, which should run code that supports autoscaling. In addition, the pricing model works in such a way that you are not charged when no operations are performed, unlike PaaS or containers.

Also read: Securing Your Microservices Architecture: The Top 3 Best Practices

Working with different file types

Serverless works perfectly with files in most formats, such as video, image, text or audio. You can perform various functions such as data transformation and cleaning. In addition, word processing such as PDF processing, sound manipulation such as audio normalization, or video and image processing is also possible.

Calculating Parallel Tasks

Each parallel task is an excellent example of serverless runtime, and each parallel task leads to one action. Such tasks may include searching and sorting objects stored in the cloud, such as web scraping or map edits. You can also perform complex tasks, such as automating business processes or tuning hyper parameters.

Robust foundation for streaming applications

With FaaS, it is possible to build a solid foundation for the real-time creation of data pipelines and streaming apps. It is compatible with all types of data streams, including log data for IoT and other applications, validation, cleansing, enrichment and transformation.

Testing Service Continuity

You can set up FaaS, like AWS Lambda functions, to make API calls to your services, just like API calls by users. You can even create a mock flow of traffic to the services in production with FaaS.

These are good practices to periodically test your service continuity. Any outages you encounter will be visible in your monitoring tool, so you’ll be aware of any outages or performance drops.

Serverless pipelines for continuous deployment

You can use serverless to enhance the continuous integration and continuous delivery (CI/CD) process and automate the entire process from merging pull requests to deploying to production. And since FaaS features are cost-effective and easy to set up, DevOps engineers can focus on other parts of the infrastructure and further reduce costs.

Also read: Effectively Using Low-Code/No-Code in the Developer Cycle

Benefits of Using DevOps with Serverless Computing

Serverless computing has the potential to transform IT operations. By extending its property of charging based on function calls, developers can enjoy various applications. Some of the other great benefits of serverless computing include:

  • Infinite scalability: With serverless computing, you can scale features horizontally and elastically based on user traffic.
  • NoOps: Infrastructure management in serverless computing is completely outsourced, so your internal teams only need to focus on the operational tasks.
  • No inactivity charges: Older cloud computing models charge you per hour to run virtual machines. With a serverless computer model, you only have to pay for the execution time and the number of functions performed.

Disadvantages of Serverless Computing

Serverless computing has enabled a variety of operations and organizations can run many different applications on it. However, it may not be the best choice for specific applications, leading to some potential drawbacks, including:

  • Stable or predictable workloads: Serverless provides the most cost-effective model for unpredictable workloads. However, for stable workloads with predictable performance requirements, this feature is not necessary. Instead, traditional systems are a better fit because they are much simpler and can be cheaper than serverless in such cases.
  • Cold start: Serverless architectures are optimized for scaling up and down to zero, but not for long-running processes, meaning they may start from scratch for a new request. This can cause noticeable boot latency, which may not be acceptable for specific users.
  • Monitor and debug: A serverless architecture exacerbates the complexity of already challenging operational tasks. For example, debugging tools are not typically updated to meet the requirements of serverless computing.

The future of serverless computing in DevOps

Serverless computing works uniquely well with DevOps, enabling faster access to a wide variety of applications at lower cost and architecture complexity. Developers rely on it because of its various features that provide various unique features.

The concept of serverless computing is constantly evolving to solve more and more development and operational challenges. While there are certain challenges that need to be addressed, eventually the tools and strategies in serverless will adapt to better serve DevOps. Today, most major cloud service providers are betting on serverless, and we can expect more optimized solutions in the future.

Read next: Best DevOps Monitoring Tools for 2022

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