Though the technology is advanced, we all have faced problems. Mainly while using internet we have all faced and still facing network problem. The main reason for this is its server, whenever this server gets problem we have to face it, but what if there are no servers? Yes, but it is not possible to completely avoid servers but its use can be reduced using serverless computing.
Serverless computing is a method of providing backend services on an as-used basis. A serverless provider allows users to write and deploy code without the hassle of worrying about the underlying infrastructure. A company that gets backend services from a serverless vendor is charged based on their computation and do not have to reserve and pay for a fixed amount of bandwidth or number of servers, as the service is auto-scaling.
Serverless computing does not hold resources in volatile memory; computing is rather done in short bursts with the results persisted to storage. When an app is not in use, there are no computing resources allocated to the app. Pricing is based on the actual amount of resources consumed by an application. It can be a form of utility computing.
Despite the name serverless, physical servers are still used but developers do not need to be aware of them.
Serverless computing allows developers to purchase backend services on a flexible ‘pay-as-you-go’ basis, meaning that developers only have to pay for the services they use.
In the early days of the web, anyone who wanted to build a web application had to own the physical hardware required to run a server, which is expensive undertaking.
Then came cloud computing, where fixed numbers of servers or amounts of server space could be rented remotely.
Developers and companies who rent these fixed units of server space generally over-purchase to ensure that a spike in traffic or activity will not exceed their monthly limits and break their applications. This means that much of the server space that gets paid for can go to waste. Cloud vendors have introduced auto-scaling models to address the issue, but even with auto-scaling an unwanted spike in activity, such as a DDoS Attack, could end up being very expensive.
The first “pay as you go” code execution platform was Zimki, released in 2006, but it was not commercially successful. In 2008, Google released Google App Engine, which featured metered billing for applications that used a custom Python framework, but could not execute arbitrary code. PiCloud, released in 2010, offered FaaS support for Python.Google App Engine, introduced in 2008, was the first abstract serverless computing offering.
The most common use case of serverless is supporting microservices architectures. The microservices model is focused on creating small services that do a single job and communicate with one another using APIs. While microservices can also be built and operated using either PaaS or containers, serverless has gained significant momentum given its attributes around small bits of code, inherent and automatic scaling, rapid provisioning, and a pricing model that never charges for idle capacity.