Serverless Computing Definition & Meaning
Serverless computing is a cloud computing method where the cloud service provider offers server capacity and performance to users on an as-used basis. The cloud provider runs the server infrastructure and dynamically manages the allocation of computing resources based on demand from end users, billing users only for resources used, a business model similar to utilities charging customers for electricity.
Serverless computing scales automatically and allows developers and users to write code and deploy applications without having to be concerned about the underlying infrastructure. Pricing is based on the amount of resources consumed by an application rather than reserving and paying for a fixed amount of bandwidth or number of servers.
It's important to note while the term is coined "serverless," physical servers are still in use—although developers and IT admins do not need to be aware of them. Serverless computing is used to simplify the process of deploying applications into production and can be used in combination with code deployed in more traditional styles, such as microservices.
Advantages of serverless computing
- Lower cost: Using traditional cloud providers of backend services can result in the user paying for unused space or idle CPU time. Serverless computing can be considered pay-as-you-go computing, as the user is charged for the time and memory to run the code without fees for idle time
- Scalability: The cloud provider is responsible for scaling capacity, not developers and operators. This means the serverless vendor manages scaling up or down on demand.
- Increased productivity and efficiency: Serverless architecture cuts down significantly on time to market. With Function as a Service, developers can create simple, event-driven functions. This means backend software development is simplified because the programmer doesn't have to worry about multithreading or HTTP requests in the coding.
Disadvantages of serverless computing
- Resource limits: Serverless computing does not work well for high-performance computing workloads because of the resource limitations of cloud providers.
- Diagnoses: Diagnosing performance or excessive resource usage problems using a serverless architecture could pose more problems than with traditional server code because of the inability to dive into detail.
- Privacy: A lot of serverless computing environments are based on public cloud environments, raising the potential for data privacy issues that might arise under regulations like GDPR.
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