Introduction to Serverless Computing

An organization’s conventional approach to creating an IT environment/infrastructure by getting hardware and software resources individually has become outdated. With the advent and there are now a variety of ways to virtualize IT systems and access required applications over the Internet through web-based applications.

Cloud computing is all the rage today and with many competitive cloud service providers in the market, there are a lot of queries on which one to choose. Before diving deep into understanding Serverless Compute, refresh your memory with some cloud concepts.

Introduction to Serverless Computing:

Serverless computing is a cloud computing code execution model in which the cloud provider handles the functioning/operations of virtual machines as needed to fulfil requests, which are billed by an abstract calculation of the resources required to satisfy the request rather than per virtual machine, per hour. Despite the term, it does not involve the execution of code without the use of servers. The name “serverless computing” comes from the fact that the owner of the system does not have to buy, rent, or provision servers or virtual machines on which the back-end code runs.

Why Serverless Computing?

Serverless computing can be more cost-effective than renting or buying a fixed number of servers, which also results in large periods of underuse or idle time.

In addition, a serverless architecture means that developers and operators do not need to spend time setting up and tuning autoscaling policies or systems; the cloud provider is responsible for scaling the capacity to the demand.

As the modern cloud-native architecture inherently scales down as well as up, these systems are known as elastic rather than scalable.

The units of code revealed to the outside world with function-as-a-service are basic event-driven functions. This eliminates the need for the programmer to think about multithreading or explicitly handling HTTP requests in their code, simplifying the task of back-end software development.

Top Serverless Computing Tools:

1. AWS Lambda

AWS Lambda was the first serverless computing tool introduced in 2014 popularly known as Function-as-a-Service or FaaS.

• AWS Lambda is a serverless computing service that allows you to run code without having to provision or manage servers, develop workload-aware cluster scaling logic, manage event integrations up to date, or manage runtimes.

Benefits:

• No need to maintain servers: AWS Lambda runs your code without the need for you to provision or manage resources. Simply write the code and upload it as a ZIP file or a container image to Lambda.
• Continuous scaling: AWS Lambda scales your application automatically by executing code in response to each occurrence. Your code runs in parallel and processes each trigger separately, scaling to the workload’s scale, from a few requests per day to hundreds of thousands per second.
• Millisecond metering reduces costs: With AWS Lambda, you only pay for the computing time you use, so you’re never overpaying for infrastructure. You’ll be paid for every millisecond your code runs as well as the number of times it’s triggered.
• Consistent output at any scale: With AWS Lambda, you can reduce the time it takes for your code to run by selecting the appropriate memory size for your function.

How it works:

Image Source: docs.amazon.com

2. Azure Functions: 

• Azure Functions is a serverless computing platform that allows you to write less code, manage fewer resources, and save money. Rather than thinking about installing and managing servers, the cloud infrastructure contains all the current tools necessary to keep the functioning of the application.
• Concentrate on the code that matters most to you, and Azure Functions will take care of the rest.
• We often design systems to react to a sequence of critical events. Any programme, whether it’s creating a web API, reacting to database updates, processing IoT data streams, or even handling message queues, requires a way to run code as events occur.
• To meet this need, Azure Functions provides “compute on-demand” in two significant ways.
• First, Azure Functions allows you to transform your system’s logic into easily accessible code blocks. These code blocks are called functions.
• Second, as demand grows, Azure Functions scales up to meet the demand with as many resources and function instances as are required – but only for as long as they are needed. Any extra resources and programme instances are automatically deactivated as requests decrease.

Benefits:

• Simplified development
• All the power of Azure Web Apps is available in Azure Functions
• No server is there to maintain
• It is used to break the monolithic architecture into loosely coupled functions
• Independent scaling

Here is a step-by-step guide for creating an Azure Function. 

3. Google Functions: 

• Google Cloud Functions is a serverless platform for developing and linking cloud services.
• Cloud Functions allows you to create quick, single-purpose functions that are linked to events generated by your cloud infrastructure and services.
• Your code runs in a  fully managed environment.
• On Google Cloud Platform, Cloud Functions can be written in JavaScript, Python 3, Go, or Java.

Benefits:

• Event-Driven
• Automatic Scaling
• No Server Management
• Pay what you use
• Integrated Services

There are various parameters to consider before making the right choice. It depends on the requirement, IT environments, problems to be solved, cost, etc. Let us look at a real-life case study to understand the implementation of serverless computing tools.

Case Study: Architecting Serverless Web Application on AWS

A client of CloudThat wanted to design and architect a solution for creating a web application that is going to be used as a platform to conduct online exams. These exams can either be on-demand where a candidate can visit the website anytime and take the exam or the exam starts at a specific date & time where thousands of candidates will attempt the exam simultaneously. The solution must be deployed on AWS and should be able to handle large variation in traffic with minimum cost. Read more about the case study here. 

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WRITTEN BY Daneshwari Mathapati