Serverless computing has rapidly earned a lot of fame in event-driven programming. Serverless computing is a disruptive application development paradigm which eases the programmers from spending time to think about different ways to scale their hardware.

Serverless computing is a model in which execution of code is responsibility of the cloud service provider which allocates the resources to the code dynamically. The code usually runs inside stateless containers which are triggered by different events like https requests, databases events, queuing services, file uploads etc. The code that is sent to a cloud service provider by a developer is commonly in the form of a single function. Therefore, serverless is also referred to as “Functions as a Service” or “FaaS”. Following are the “FaaS” services of major cloud service provides.

• AWS
• Microsoft Azure
• Google Cloud

How Serverless Computing Got Where It Is Today?

For the past ten years, software teams ran away from the practice of directly managing hardware in data centers, and they choose to rent compute capacity from “infrastructure as a service” vendors like Microsoft Azure and AWS. As managing hardware directly didn’t give much benefit to any software team, so unloading this heavy burden to an IaaS vendor was warmly welcomed by software companies around the globe.

The first step of moving to an IaaS involved duplicating data center practices in the cloud. For instance, a team having a dozen machines in its data center might generate 10 virtual machines in an IaaS and then copy each server to the cloud one by one separately. This concept worked really well, but it didn’t take much time for the industry to perceive that IaaS is not only to unload hardware management. Infact, it is a basically a different way to build apps, offering much more greater opportunities.

The very next step in this amazing journey is the Serverless Computing. With serverless computing instead of allotting virtual machines and deploying code in them, you can upload functions to IaaS and IaaS vender will figure out itself which functions to run and how to run. The infrastructure scaling is done by the IaaS provider to make sure that the function performs the way they are expected, even if they are called frequently. The only thing a software team has to do is write code and upload it to the IaaS vendor.

Importance of Serverless Computing as A Paradigm

Serverless computing promises the software team to free them from the stress of thinking about the machine on which code executes: the number of machines needed at the peak times, whether or not those machines have been patched, do these machines have the adequate security setting and so on. The software team just has to focus on making the code great and the rest is done by the IaaS vendor. Following are some advantages:

Ideal for event-driven scenarios: During both up-scaling and down-scaling, the conventional auto-scaling feature can have critical warm-up times for clusters and there can be a chance that it may not be in continuation. Serverless provides a complete computing model when there is a need to execute small blocks of code (functions) as they turn out to be the response to event triggers and you only have to pay for the chunks of resource time which you have consumed. For example, with traditional architecture, you created a server with 100 GB of memory but you are only using 10 GB of it, still you will have to pay for the 90 GB memory that you are not using. Now in serverless computing, if you are using only 10GB of memory, you’ll pay only for the 10 Gb you are using. Also, with the core component of Amazon’s serverless platform which is AWS lambda, you pay only for the time your code runs. Let’s say if your code is runs for 100 milliseconds, you are charged only for that 100 milliseconds. This helps in saving a lot of expenses and it is best for event-driven architectures such as the IoT scenarios.

Assembling cost-effective microservices architecture: Serverless help in executing a lot of cloud computing functions simultaneously. All these functions have no relation to each other in reaction to the event mirroring that is simultaneously in execution. The small blocks of code that is deployed in serverless computing is easy to manage, test and debug. Software teams can easily put together an architecture mirroring micro-services by setting up and deploying several cloud functions that work together. Many leading developers are using this strategy to deploy software by using a cost-effective way.

Is Serverless The Future?

Before we answer this, we suggest you to check this webpage that is the product section of AWS page. Now if you’ll see carefully, there are about 100 plus “as a service product” that cover the total software development life cycle, from beginning (development) to end (deployment and maintenance).

Now what do you think of this version of Amazon Web Service? We truly believe that AWS will do their best to change the world by using serverless. In the future we will be able to develop, test, deploy and maintain our application by using their solutions. We will only have to pay for the time we consume their services and not get charged for the idle time.

Now if you look out the growth of AWS services in the past couple of years, they are swiftly extending their domains and increasing their offerings to cover approximately all the requirement that are in the software Lifecyle process. While some of those services are not a hundred percent complete yet but within next 2 to 3 years, AWS will make sure that you don’t have a second thought to go for cloud offerings when choosing infrastructure for your app’s development, testing, deployment and maintenance.

So, what is the objective of these cloud service providers as far as software life cycle is concerned? Well, answer is obvious their aim is to cover all the software requirements and processing under the umbrella of serverless computing and offer their clients a safe zone, in which they can focus on their business logic and put their maximum efforts to achieve maximum results.