If Cloud Computing reduced the need of plumbing, Serverless Computing makes it affordable by reducing the overall cost of ownership.
Serverless Computing is a framework to trigger code in response to a set of pre-defined events. Enterprise organizations are exploiting serverless to achieve the desired scale needed, without the overhead of running and managing servers. The best thing about serverless computing is developers need not rely on ops to deploy their code. They are able to rapidly run and test the code in the cloud or on prem without traditional workflow.
Serverless Computing dramatically reduces the surface area of an application running in production. With no servers to provision, software to upgrade, and operating systems to patch, the whole focus shifts to the code. Businesses can stay focused on what’s important for their customers.
Serverless Computing does not require additional metadata to describe the runtime requirements. Outside of the developer defining the maximum memory threshold, and adjusting the time window of execution, the environment has nothing to configure.
Serverless Computing dramatically reduces the time to push changes to the production through faster CI/CD cycles. Since developers focus on one function at a time, the build time is reduced to a fraction. This doesn’t mean that the code will bypass the regular checks and tests that are a part of build pipelines. The extensive build process gets broken down into dealing with smaller, sizable chunks of code resulting in rapid deployment.
Most of the Serverless Computing platforms focus on the source code than pre-packaged binaries. This brings flexibility to the development and deployment processes. The code can include all the dependencies including the native, proprietary or third-party libraries that are required by the component/service.
One of the key attributes of Serverless Computing is the ability to support multiple runtimes, languages, and frameworks. Developers can choose best of the breed languages to implement the fine-grained functionality. Independently deployable units will be connected at runtime to deliver the required workflow. That means developers are not restricted to one language or runtime to implement the logic.
The fundamental difference between PaaS and Serverless Computing lies in the way the code is executed. Developers write code that is autonomous and independent of other components and services. Each component is invoked only when an event takes place. By connecting the dots, developers can define the sequence in which the invocation happens at runtime. For example, developers can easily change the logic of sending a push notification to a device instead of a text message without changing a single line of code. All they got to do is to change the flow of events.
A few argue that Serverless Computing is not as scalable as traditional IaaS or PaaS. But if implemented right, it can deliver ultimate elasticity. For example, AWS Lambda imposes a few restrictions on the concurrency limit. It has a default safety throttle of 100 concurrent executions per account per AWS region. This can be increased by filing a service limit increase request with AWS support. With the right design and approach, customers can easily move the business logic deployed in a fleet of EC2 servers to AWS Lambda.
A typical DevOps team handles a variety of tasks ranging from provisioning, build management, integration, deployment, and monitoring. Serverless Computing impacts most of these functions. There are no server resources to provision; build management, packaging, and deployment are left to the runtime, and there are very few parameters and metrics to monitor. Software build management tools such as Jenkins can be easily integrated with serverless environments to deploy code right after each commit. Since the OS and runtime are managed by the platform vendor, there is nothing to patch or update. This attribute of Serverless Computing is occasionally referred to NoOps.