If you want your teams to focus on front-end development and services, you might consider serverless computing. Serverless computing refers to outsourcing IT infrastructure to external providers.
It features a flexible use of resources that can be scaled based on real-time requirements. As a result, it is favored by DevOps thanks to its quicker 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. As a result, developers get more time to develop front-end applications and business logic. This eases up on the workload of your teams and ensures their maximum focus is on innovation.
Though technically, it does use servers, it is called serverless computing because the servers, in this case, are hosted by a third-party service provider, making them seemingly non-existent to 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 core of cloud-native application development.
How are Serverless Platforms Improving DevOps?
The serverless model is better suited for certain customers than the IaaS and SaaS models, which demand a fixed monthly or yearly price. Sometimes developers do not need to use the entire capacity offered by their cloud solution.
In such cases, serverless computing provides a fine-grain, pay-as-you-go model, so you only pay for the resources consumed for the life of the called function. This can lead to significant reductions in the projected cost, allowing for greater savings over other cloud models for many workloads.
However, serverless models can only be considered an evolving technology at best. Considering it as a universal solution for development and operations problems, this model can lead to certain drawbacks.
That being said, IT professionals have reported using serverless for a large array of applications, including customer relationship management (CRM), finance, and business intelligence.
Popular Applications of Serverless Computing
Many major cloud service providers offer serverless platforms to users who can finally enjoy a NoOps state, including Amazon, Google, and Microsoft. Serverless platforms by Alibaba, IBM, and Oracle, among others, are soon to follow. At the same time, open-source projects such as OpenFaaS (function as a service) and Kubeless are bringing serverless technologies to on-premises architecture.
Get support for microservice architecture
One key usage of serverless computing is its support for microservice architectures, which enable the creation of small services with a singular job that can use APIs to connect to one another.
Serverless is uniquely suited for this model, which needs to run code that supports automatic scaling. Plus, the pricing model functions in such a way that you aren’t charged when no operations are running as opposed to PaaS or containers.
Work with different file types
Serverless works perfectly well with files in most formats such as video, image, text, or audio. You can carry out various functions such as data transformation and cleansing. In addition, text processing, such as PDF processing, sound manipulation like audio normalization, or video and image processing is also possible.
Compute parallel tasks
Any parallel task presents an excellent example for serverless runtime, and each parallel task triggers one action. Such tasks may include searching and sorting objects stored on the cloud, such as web scraping or map operations. Further, you can perform complex tasks like business process automation or hyperparameter tuning.
Robust foundation for streaming applications
Using FaaS, it is possible to build a steady foundation for the real-time creation of data pipelines and streaming apps. It is compatible with all kinds of data streams, including log data for IoT and other applications, validation, cleansing, enrichment, and transformation.
Test service continuity
You can set up FaaS, such as AWS Lambda functions, to make API calls to your services, much like API calls made by users. You can even create a mock flow of traffic to the services in production using FaaS.
These are good practices to test your service continuity periodically. Any failures that you might encounter are visible in your monitoring tool, so you are aware of failures or any performance drops.
Serverless pipelines for continuous deployment
You can use serverless to improve the CI/CD (continuous integration and continuous delivery) process and automate the entire process, from merging pull requests to deploying in production. And since FaaS functions are cost-efficient and easy to set up, DevOps engineers can focus on other parts of the infrastructure and further reduce costs.
Advantages of Using DevOps with Serverless Computing
Serverless computing has the potential to transform IT operations. By extending its property of levying charges based on function calls, developers can enjoy several applications. Some of the other major benefits of serverless computing include:
- Infinite scalability: Serverless computing allows you to scale functions horizontally and elastically based on the user traffic.
- NoOps: Infrastructure management in serverless computing is completely outsourced, so your in-house teams only need to deal with the operational tasks.
- No idle time costs: Legacy cloud computing models charge you per hour for running virtual machines. With a serverless computing model, you only need to pay for the execution duration and the number of functions executed.
Drawbacks of Serverless Computing
Serverless computing has enabled a range of operations, and organizations can run many different applications on it. However, it might not be the best choice for specific applications, which leads to a few possible disadvantages, including:
- Stable or predictable workloads: Serverless offers the most cost-effective model for unpredictable workloads. However, steady workloads with predictable performance requirements do not need this feature. Instead, traditional systems suit it better as they are much simpler and can be cheaper than serverless in such cases.
- Cold starts: Serverless architectures are optimized for scaling up and down to zero, but not long-running processes, meaning they might be starting up from zero for a new request. This might cause a noticeable startup latency, which might not be acceptable to specific users.
- Monitoring and debugging: A serverless architecture aggravates the complexity of the already challenging operational tasks. For example, debugging tools are typically not updated to meet the requirements of serverless computing.
The Future of Serverless Computing in DevOps
Serverless computing works uniquely well with DevOps, opening a vast array of applications faster at lower cost and complexity of architecture. Developers rely on it for its various functions which offer several unique features.
The concept of serverless computing is constantly evolving to solve more and more development and operational challenges. Though there are certain challenges that need addressing, the tools and strategies in serverless will eventually adapt to serve DevOps better. Today, most major cloud service providers are betting on serverless, and one can expect better-optimized solutions in the future.
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