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Serverless Computing: How It Works and Why It Matters

Serverless Computing: How It Works and Why It Matters

21/03/2024

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For developers, wrestling with server management can feel like an unwelcome detour on the road to building great applications. Provisioning, configuration, scaling – it’s a time-consuming headache. But what if there was a way to develop and deploy code without ever having to touch a server? Enter serverless computing, a revolutionary approach that’s transforming the development landscape. In this blog, we’ll unpack the magic of serverless computing, how it works behind the scenes, and why it should be on your radar.

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. Pricing is based on the actual amount of resources consumed by an application, rather than on pre-purchased units of capacity. It can be more cost-efficient than traditional cloud computing models for many applications, particularly those that experience varying levels of demand.

In serverless computing, developers can build and run applications and services without managing infrastructure. Your application still runs on servers, but all the server management is done by the cloud provider. This allows developers to focus on their core product without worrying about managing and operating servers or runtimes, either in the cloud or on-premises.

What is Serverless Computing in Azure?

Serverless computing in Azure is primarily offered through Azure Functions and Logic Apps, enabling developers to build applications that scale with demand without managing infrastructure. These services allow you to focus on writing code and business logic, while Azure takes care of the underlying servers, scaling, maintenance, and high availability.

Serverless Computing Architecture

Serverless cloud computing enables developers to focus on individual functions, leading to its common classification as Functions as a Service (FaaS). Here’s how functions are crafted and run in a serverless environment:

Developers craft a function tailored to meet a particular requirement within the application’s codebase.
Next, they specify an event that prompts the cloud service provider to activate the function. An HTTP request is often used as this trigger event due to its ubiquity.
The specified event is then activated, such as by a user clicking a link if the event is an HTTP request.
Upon activation, the cloud service provider evaluates if the function’s instance is already active. If not, it initiates a new instance for that function.
Finally, the outcome of the function is delivered back to the user within the application, completing the process.

Pros and Cons of Serverless Computing

Benefits of Serverless Computing

Serverless computing offers a myriad of benefits that are transforming the way businesses and developers approach application development and deployment. By leveraging serverless computing, organizations can achieve greater scalability, cost efficiency, and development agility, among other advantages.

#1 Cost Efficiency

Serverless computing significantly reduces costs by eliminating the need for pre-provisioning servers. Organizations pay only for the compute time they consume, which optimizes spending, especially for workloads with variable traffic.

#2 Automatic Scaling

Serverless computing automatically adjusts computing resources to match the application’s needs in real time. This means applications can seamlessly handle increases in traffic without manual intervention.

#3 Enhanced Developer Productivity

With serverless computing, developers can focus more on writing code and developing features rather than managing infrastructure. This abstraction of server management accelerates development cycles and reduces time to market.

#4 Operational Management Reduction

Serverless computing abstracts away much of the operational complexity, such as server maintenance, updates, and scaling. This reduces the operational burden on IT teams.

#5 Built-in High Availability and Fault Tolerance

Serverless platforms typically provide high availability and fault tolerance out of the box. Applications are more reliable as they can withstand failures and continue to operate smoothly.

#6 Simplified Deployment and Operations

Deploying applications is straightforward in a serverless environment, as the cloud provider takes care of the underlying infrastructure. This simplifies both deployment and ongoing operations.

Disadvantages of Serverless Computing

Just like anything else, serverless computing also has its own disadvantages. Below are some disadvantages of using serverless computing:

#1 Cold Starts

A common issue in serverless computing is the “cold start” problem, where initiating a new instance of a function can lead to higher latency for some requests, particularly after periods of inactivity.

#2 Vendor Lock-in

When using serverless computing, there’s a risk of becoming too reliant on a single cloud provider’s tools and services, making it challenging to migrate to another provider without significant modifications.

#3 Limited Control Over the Environment

Developers have less control over the server and runtime environment in serverless computing, which can be a limitation for applications requiring specific configurations or customizations.

#4 Complexity in Monitoring and Debugging

Serverless applications can be more challenging to monitor and debug due to their distributed nature and the abstraction of the underlying infrastructure.

#5 Performance Considerations

For high-performance applications that require consistent response times, the variable performance characteristics of serverless computing, including cold starts, can be a drawback.

#6 Cost Predictability

While serverless computing can be cost-effective, it can also be challenging to predict costs, especially for applications with unpredictable workloads, due to the pay-per-use pricing model.

#7 Limited Resource Configurations

Serverless functions typically have limitations in terms of execution time, memory, and processing power, which may not be suitable for long-running or resource-intensive tasks.

Serverless Computing Examples in Real World

#1 Web Applications

Serverless computing examples are increasingly prevalent across various industries, showcasing the versatility and efficiency of this technology. Companies leverage serverless architectures to build and deploy web applications without the hassle of managing servers. For instance, a news website might use serverless functions to dynamically render articles and content based on user requests. Hence, scaling automatically during high-traffic periods, such as breaking news events.

#2 IoT

Another serverless computing example is in the realm of IoT. IoT devices, ranging from smart home sensors to industrial monitoring equipment, generate vast amounts of data. Serverless computing can process and analyze this data in real time, triggering alerts or actions based on predefined conditions without the need for continuous server monitoring. This approach significantly reduces the overhead and costs associated with data processing for IoT apps.

#3 File Processing

A third example of serverless computing is evident in file processing systems. Companies often need to process uploaded content, such as images, videos, or documents. Using serverless functions, these files can be automatically resized, converted, or analyzed as soon as they’re uploaded, without maintaining a dedicated server for these tasks. This not only speeds up the processing but also optimizes resource usage based on the volume of uploads.

#4 E-commerce Personalization

In the e-commerce industry, serverless computing examples shine through in personalized user experiences. Serverless functions can analyze user behavior in real-time, offering personalized recommendations, discounts, and content without constant server computation. This dynamic personalization enhances user engagement and sales while keeping operational costs low.

#5 Scalable APIs

Serverless computing is instrumental in creating scalable APIs. APIs built on serverless infrastructure can handle requests ranging from a few to millions without any change in the underlying architecture. This scalability is crucial for startups and enterprises alike, allowing them to adapt to user demand rapidly.

Top 5 Serverless Computing Platforms

Each serverless computing platform provides unique features and benefits tailored to different use cases and developer preferences. Here’s a detailed look at the top 5 serverless computing platforms:

#1 AWS Lambda

AWS Lambda allows developers to run code in response to events without managing servers. Lambda automatically scales the application by running code in response to each trigger, charging only for the compute time consumed. This serverless computing platform integrates seamlessly with other AWS services. As a result, it a robust choice for building complex applications within the AWS ecosystem.

#2 Microsoft Azure Functions

Azure Functions is a key player in the serverless computing platform space. It enables developers to run event-driven functions in a wide range of programming languages. This platform offers built-in development tools and integrates deeply with Azure services and external resources. It provides a flexible environment for building and deploying serverless applications. Azure Functions stands out for its enterprise-grade features and seamless integration with DevOps practices.

#3 Google Cloud Functions

Cloud Functions, excels in providing a scalable environment for running backend code in response to HTTP requests and other event triggers. It’s deeply integrated with Google’s cloud services and data analytics tools. Hence, making it an attractive serverless computing platform for applications requiring advanced data processing and analytics capabilities.

#4 IBM Cloud Functions

IBM Cloud Functions is a versatile serverless computing platform that supports a variety of programming languages and custom Docker containers. This platform is designed for executing code in response to a range of event sources, offering a good mix of flexibility and integration capabilities with IBM’s Watson and IoT services, making it ideal for AI-driven and IoT applications.

#5 Alibaba Cloud Function Compute

Function Compute offers a fully managed event-driven service that allows developers to focus on writing code without managing servers. It supports a broad range of triggers, making it a comprehensive serverless computing platform for building and deploying applications at scale.

Serverless Computing vs Microservices

Feature	Serverless Computing	Microservices
Infrastructure Management	Fully managed by the cloud provider. No need for server provisioning or maintenance.	Requires management but allows for containerization and orchestration tools like Kubernetes.
Scalability	Automatically scales with demand, ideal for variable workloads.	Scalable, but each service needs to be scaled independently, which can be more complex.
Cost	Pay-per-use model, only paying for the compute time used.	More constant cost due to running services, but can optimize through efficient resource allocation.
Development Focus	Focuses on writing function code that reacts to events.	Focuses on developing independent services, each with its own business logic.
Granularity	Finer granularity with functions typically performing single, small tasks.	Coarser granularity, with each service handling a segment of business functionality.
Event-Driven	Inherently event-driven, with functions triggered by specific events.	Can be event-driven but requires additional setup for event handling and communication.
Technology Stack	Limited by the cloud provider’s supported languages and tools.	Flexibility to use different technology stacks for different services.
Deployment and Lifecycle Management	Simplified deployment, with the cloud provider handling most of the lifecycle management.	More complex, requiring continuous integration/continuous deployment (CI/CD) pipelines and service orchestration.
State Management	Stateless, with state management needing external services.	Services can maintain their state, but external databases are often used for scalability and resilience.
Inter-Service Communication	Limited to the mechanisms provided by the cloud provider.	Flexible, can use various protocols and patterns like REST, gRPC, or messaging queues.

Serverless Computing vs PaaS

Feature	Serverless Computing	PaaS
Infrastructure Management	Fully managed by the cloud provider. No infrastructure management required by the user.	The cloud provider manages the infrastructure, but users may have to manage some aspects of the application environment.
Scaling	Automatically scales up and down instantaneously based on demand, without user intervention.	Scales automatically or with minimal user configuration, but may not be as instantaneous or fine-grained as serverless.
Pricing Model	Pay-per-use, based only on the resources consumed during function execution.	Generally subscription-based, paying for reserved capacity regardless of usage.
Runtime Environment	Limited control over the runtime environment. The cloud provider determines the underlying OS and middleware.	More control over the runtime environment, allowing for custom runtime configurations.
Operational Responsibilities	Developers focus entirely on code; the platform handles deployment, scaling, and infrastructure maintenance.	Users are responsible for application deployment, runtime configurations, and sometimes scaling settings.
Startup Latency	Can experience cold starts, leading to variable latency during function initialization.	Typically lower startup latency as the application is always running, although this can depend on the specific PaaS offering.
Use Case	Ideal for event-driven applications, microservices, and workloads with variable traffic.	Suited for a wide range of applications, especially those requiring specific runtime environments and continuous operation.
Granularity	Fine-grained, with applications broken down into individual functions.	Coarser granularity, with applications deployed as one or more interconnected services.

Serverless Computing vs Edge Computing

Feature	Serverless Computing	Edge Computing
Definition	A cloud-computing execution model where the cloud provider dynamically manages the allocation of machine resources.	A distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth.
Infrastructure Management	Managed by the cloud provider, freeing developers from managing servers or infrastructure.	Requires management of numerous edge locations, which can be more complex than traditional data center management.
Primary Focus	Reducing the operational complexities and costs associated with infrastructure management.	Reducing latency and bandwidth use by processing data closer to the source or user.
Scalability	Automatically scales with demand, managed by the cloud provider.	Scalability depends on the edge infrastructure in place and may require more manual scaling strategies.
Execution Environment	Functions execute in a stateless container managed by the cloud provider.	Runs on edge devices or servers located near the data source or user, often in a more constrained or specialized environment.
Data Processing Location	Processing is done in the cloud, with data transmitted to and from the cloud provider’s data centers.	Processing is done closer to the data source, minimizing the need to transmit data to a centralized location.
Latency	Potentially higher latency due to the distance between users and cloud data centers.	Lower latency due to proximity to data sources and end-users.
Cost Model	Pay-per-use, based on the amount of compute resources consumed by the functions.	Costs associated with setting up and maintaining edge infrastructure, but can reduce data transmission costs.

Cloud Computing vs Serverless Computing

Feature	Serverless Computing	Cloud Computing
Infrastructure Management	Fully managed by the cloud provider; no need for users to manage or provision servers.	Users may need to manage and configure the infrastructure, depending on the service model (IaaS, PaaS, SaaS).
Scaling	Automatic and instant scaling based on the application’s demand, without user intervention.	Scalable, but often requires manual scaling or auto-scaling configuration.
Pricing Model	Pay-per-use, based on the actual amount of resources consumed by the functions.	Typically pay-per-hour or pay-per-minute for reserved resources, regardless of usage.
Development Model	Focuses on writing and deploying code for individual functions that respond to events.	Involves deploying applications or services onto cloud infrastructure, which can include servers, containers, or platforms.
Startup Time	Functions may experience a “cold start” delay when initially invoked or when scaling from zero.	Applications are always-on, reducing startup latency but incurring continuous costs.
State Management	Functions are stateless; maintaining state requires external services.	Applications can maintain state internally or use external services, depending on the architecture.
Granularity	Fine-grained, with applications broken down into individual functions.	Coarse-grained, with applications deployed as monoliths or microservices.
Control	Limited control over the underlying infrastructure and runtime environment.	Greater control over the environment, including the choice of operating systems, middleware, and runtime.
Use Cases	Ideal for event-driven architectures, microservices, and workloads with variable traffic.	Suitable for a wide range of applications, from websites and databases to complex enterprise systems.

The Future of Serverless Computing

Serverless computing offers a compelling alternative to traditional application development. By freeing you from server management headaches, it allows you to focus on what truly matters: building innovative features and functionalities. The automatic scaling and pay-per-use model ensure your applications are always optimized for cost and performance. Whether you’re a seasoned developer or just starting out, serverless computing is worth exploring.

Ready to experience the benefits of serverless computing firsthand? Look no further than AMELA Technology‘s cutting-edge cloud computing service. Our team of experts can help you seamlessly integrate serverless architecture into your development workflow, empowering you to build and deploy applications faster than ever before.

Contact us through the following information:

Hotline: (+84)904026070
Email: hello@amela.tech
Address: 5th Floor, Tower A, Keangnam Building, Urban Area new E6 Cau Giay, Pham Hung, Me Tri, Nam Tu Liem, Hanoi

Editor: AMELA Technology