What is Serverless Computing?
Serverless computing is a cloud computing execution model in which the cloud provider runs the server and dynamically manages the allocation of machine resources. With serverless, the cloud provider is responsible for the server and the user is responsible for the code. This means that the user can build and run applications and services without having to manage infrastructure. This can include functions, event-driven architectures, and more. The term “serverless” can be misleading, as servers are still involved in the process, but the management and provisioning of servers is abstracted away from the user.
Serverless Computing in Azure:
With Azure Serverless Computing, you can build end-to-end serverless solutions without managing the infrastructure with ease by using developer tools and built- in application software's from the marketplace to build, develop and deploy your applications in the cloud.
Which service provides Serverless Computing in Azure?
Microsoft Azure have a variety of options to choose from depending on your specific need and business requirement you can choose services like Azure Functions Apps, Azure Logic Apps, Azure Kubernetes Service (AKS), Azure Containers, etc.
👉 Serverless Cloud Computing Quiz Question and Answers
Characteristic features of Serverless Computing:
The main characteristics of serverless computing are:
- Event-driven: Serverless computing is based on triggers, such as a user request or a change in a data source, that trigger the execution of code.
- Fully managed: The cloud provider manages the underlying infrastructure, including servers, storage, and networking.
- Scalable: The cloud provider automatically scales the number of instances based on the incoming traffic.
- Pay-per-use: The user pays for the exact amount of computing resources used, rather than for a set of reserved resources.
- No server management: The user does not have to manage and provision servers, as the cloud provider handles that.
- High Availability: Serverless computing platform provides high availability for the functions, meaning the platform automatically handle the replication and availability of the function.
- Cold start: The function might take longer to spin up and execute when there is no active running instance of the function, this can increase the latency of the first request or the first few requests.
- Limited control over infrastructure: The user has less control over the underlying infrastructure compared to traditional cloud computing, as the user is limited to the cloud provider’s offerings.
- Short-lived compute: Serverless computing utilizes short-lived compute instances, meaning the instances spin up and tear down automatically based on the event triggering the function.
- Third-party integrations: Serverless providers often offer pre-built integrations with other services such as databases, messaging queues, and other cloud services, making it easier to build event-driven architectures with minimal code.
Benefits of Serverless Computing in Cloud:
- Serverless resources in Azure provides security for managing the serverless applications using Azure AD.
- No infrastructure or server management is required. This helps developers to focus on app functions without worrying about the backend functionality.
- Quick executions and deployments boosts the productivity.
- Reduces the cost of resources as it offers a pay as you go service. Need to pay only for the resources that we use in cloud.
- Faster CI/CD setup for easy integrations for on-going workflows.
- More efficient use of resources by automating dynamic scale up and scale down feature for better stability and performance.
Examples of Serverless Computing Services:
These are the popular cloud serverless computing services used by Cloud users.
- Azure Functions Apps
- Azure Logic Apps
- Azure Kubernetes Service (AKS)
- Azure Containers
- AWS Lambda
- Google Cloud Functions
- IBM OpenWhisk
- Oracle Functions
- Cloudflare Workers
Serverless Computing vs Cloud Computing:
Serverless computing differs from traditional cloud computing in several ways:
- Responsibility for server management: With traditional cloud computing, the user is responsible for managing and provisioning servers, while with serverless computing, the cloud provider manages the servers and dynamically allocates resources.
- Scaling: With traditional cloud computing, the user is responsible for scaling the number of servers to handle increased traffic, while with serverless computing, the cloud provider automatically scales the number of instances based on the incoming traffic.
- Pricing model: Traditional cloud computing typically charges the user for the number of servers and the amount of time they are running, while serverless computing charges the user based on the number of requests and the amount of computing resources used.
- Flexibility: With traditional cloud computing, the user has more control over the underlying infrastructure and can customize the environment to a greater extent, while with serverless computing, the user is limited to the cloud provider’s offerings and has less control over the underlying infrastructure.
- Cold Start: In Serverless function, the function has to spin up from the “cold start” situation when the function is not currently running. this can increase the latency of the first request or the first few requests.
Overall, serverless computing is more focused on the event-driven, and on-demand computing, while traditional cloud computing is more focused on providing users with a flexible, customizable infrastructure that they can use to build and run their own applications and services.