Maximizing Scalability with Serverless Computing

Contents

1. Definition and Overview
2. Key Characteristics
3. Benefits of Serverless Computing
4. Serverless Architecture and How It Works
5. Serverless Computing Platforms
6. Practical Applications and Use Cases
7. Serverless Edge Computing
8. Challenges and Considerations Benefits of Serverless Computing
9. Conclusion

1. Definition and Overview

• Imagine if your utilities, like electricity or water, were charged only for the exact amount you used instead of a fixed monthly rate. That’s how serverless computing operates. In this model, cloud providers allocate resources dynamically, allowing you to build and run applications based purely on your usage, without the need to manage the underlying server infrastructure.
• Unlike traditional cloud computing, where you need to configure and maintain physical or virtual servers, storage, and networking, serverless computing abstracts these responsibilities. The cloud provider handles everything related to server management, including resource allocation, scaling, and maintenance. As a result, developers can focus entirely on writing code and building applications without worrying about the infrastructure that powers them.
• For instance, if your application typically supports 100 to 200 users per day, but one day traffic spikes to 1,000,000 users, serverless computing will automatically scale the resources to accommodate the increased demand. In a traditional setup, this would likely result in downtime due to server overload, and provisioning additional storage or servers would take time and effort. Serverless eliminates these challenges, enabling seamless scalability and performance, even under sudden or unexpected demand.

2. Key Characteristics

Key characteristics of serverless computing include:

• Automatic Scaling: Applications scale dynamically in response to traffic or workload changes.
• Event-Driven Execution: Functions are triggered by specific events, like HTTP requests or database updates.
• No Server Management: Infrastructure is abstracted from the developer, who only focuses on code.
• Pay-Per-Use Pricing: Billing is based on the actual compute time and resources used, reducing costs for idle services.

3. Benefits of Serverless Computing

Cost efficiency

• Unlike traditional cloud computing, which requires dedicated servers and incurs costs whenever the server is idle, serverless platforms use an event-based model to charge developers. A useful analogy is using a taxi service instead of owning a car. With a taxi you don’t have to worry about the expenses spent on it such as parking fee, fuel and so on, you get to pay only for the distance you travel. In serverless computing, just like the taxi, you only pay for the computing resources you use. Unlike the traditional approach (owning a car), you are responsible for every computing resource you use, even when the system is idle.

Reduced operational complexity

• Much time is saved in serverless computing because developers don’t need to devote their time to installing and maintaining servers; they focus more on building the application. Since applications are built as independent cloud functions, one can update a function without disrupting others or the entire application.

Improved scalability

• This is one of the most important benefits of a serverless platform and why it is popular among smaller organizations and startups. Serverless platforms make it easy for developers to scale their operations automatically when demand increases. For functions that experience request fluctuations, serverless platforms scale to meet these requests by increasing or decreasing resource allocation, hence ensuring computing resources are optimized.

Faster time to market

• With serverless applications, you can release applications quickly and immediately get user feedback. This is important for startups as this reduces the time spent and the manpower needed to build applications.

Reliability

• Unlike applications hosted on dedicated servers, one can run serverless applications from anywhere. This improves application performance and reduces latency compared to traditional cloud computing.

4. Serverless Architecture and How It Works

• Serverless architecture is a way of building applications without installing and managing the infrastructure hosting the application. Developers can build serverless applications using either of the two serverless models; either using Backend as a Service (BaaS) or Function as a Service (FaaS).
• Backend as a Service (BaaS): This serverless model is usually used when building web and mobile application backends. Developers don’t need to code backend features like databases, user authentication, storage, and others. This makes development fast as developers mainly focus on the application frontend. Examples of providers offering BaaS, are Firebase, SupaBase, and AWS Amplify.
• Function as a Service (FaaS): In FaaS, developers write the code for the platform to execute without worrying about managing computing resources and scaling. This allows the execution of functions in response to triggered events. Examples of providers offering FaaS are AWS Lambda, Azure Cloud Functions, and Google Cloud Functions.

The main aim of serverless architecture is to abstract server management from developers. Here is a breakdown of how serverless platforms operate in their infrastructure.

• Function Creation: Applications built using a serverless architecture have their code written into subparts, where each part is an individual function independent of other functions and meant to achieve a specific task.
• Function Development: These functions are packaged and deployed to a serverless platform like AWS Lambda, Azure Cloud Functions, or Google Cloud Functions.
• Event-Driven Execution: These functions are invoked in response to a particular event or trigger. These triggers are either database changes, HTTP requests, or something else.
• Auto-Scaling: The serverless platforms handle the use of resources by events as workload increases or decreases. For example, the server allocates more resources when a particular function receives high traffic.
• Transient Containers: Triggering an event creates containers that allow the executed function to access required resources. After completion of a task, these containers are then destroyed.
• Billing: Billing is done based on the amount of execution time and compute resources used.
• Statelessness: Serverless functions, when executed, don’t retain information. any needed information in an invocation is saved either in a database or externally.
• Logs and Monitoring: Serverless platforms provide logging and monitoring tools to track application performance and issues in functions.

One might wonder: Isn’t serverless architecture the same as container architecture since both abstract the server from the developers? This is true, but unlike serverless functions, which completely abstract the server when building and shipping applications in containers when an application experiences high traffic, one needs to scale the containers using tools like Kubernetes. This defeats the aim of FaaS, where all actions related to the server are handled automatically by the serverless platform.

In a container architecture, you can have container instances that can run for a long period which can incur costs, unlike in serverless functions where you are billed for the amount of time your function spends when running. For smaller applications, using a serverless architecture makes it easy to decouple it into parts that the serverless platforms can run as independent functions.

5. Serverless Computing Platforms

• Before the advent of Google App Engine in 2008, Zimki offered the first “pay as you go” platform for code execution, but was later shut down. In its early stage, the Google App Engine could only support Python and featured metered billing for applications which among them was SnapChat. Around 2010, another platform called PiCloud also provided FaaS support for Python applications.
• In 2014, AWS popularized the serverless model releasing tools such as AWS Serverless Application Model (AWS SAM), and Amazon CloudWatch. Google then released their second serverless offering Google Cloud functions alongside Azure functions in 2016. Since then various serverless platforms have been released such as Function Compute by Ali Baba Cloud and IBM Cloud Functions by IBM Cloud.
• To eliminate the need for a virtualized or physical database, serverless databases have also been developed. AWS offers Amazon Aurora, a serverless version based on MySQL and PostgreSQL. Azure offers Azure Data Lake, and Google provides Firestore.

6. Practical Applications and Use Cases

Websites and APIs

• Serverless computing is used to build website applications and REST APIs. The interesting part is that applications are built using a serverless infrastructure auto-scale based on user demands, improving user experience.

Media processing

• Serverless architecture makes it easier to handle media processing. Users can upload media content from varying devices and sizes and the platform processes a single function that meets the demand of every user without reducing application performance. For example, a user can upload an image via an S3 bucket which triggers an AWS Lambda function to either add a watermark or thumbnail to the image.

Chatbots

• Developers can implement a chatbot to respond to customer questions using serverless architecture and pay only for the resources the Chatbot uses. For example, Slack uses serverless architecture to handle the varying requests of bots to avoid underutilized bandwidth due to the everyday fluctuation of customer needs.

Webhooks

• You can use a serverless platform to interact with SaaS vendors through a webhook HTTP endpoint, which receives notifications and performs tasks. This offers minimal maintenance, low costs, and automatic scaling for the built webhook.

IoT

• Coca-Cola uses serverless architectures on their vending machine Freestyle to allow customers to order, pay, and receive payment notifications for their beverages. Coca-Cola claimed they were spending around $13,000/year to operate their vending machines, which was reduced to $4,500/year after implementing serverless architectures on the vending machines.

Data processing

• Major League Baseball Advanced Media built their product Statcast with serverless architecture to provide users with accurate and real-time sports metrics. It uses serverless computing to process data and give users insights about baseball games.

Event-driven applications

• Serverless architecture is used for event-driven applications, such that when an event or a state is changed, it triggers a service. One can use serverless platforms to watch for changes to a database and compare the changes against quality standards.

7. Serverless Edge Computing

Due to the long-distance travel of data, applications take time to process requests and deliver content from centralized servers, which introduces latency and bottleneck issues.

Serverless edge computing solves this issue by distributing computing resources to multiple locations to reduce the workload on the central server. Serverless edge computing is the location of computing resources that run serverless functions closer to end users (edge). This allows the serverless application to operate on more devices, decrease congestion, and lower latency.

In serverless edge computing, infrastructure is dedicated to each device, allowing it to perform complex tasks without sending data back to the central location for processing. Here are some use cases that optimize user experience using serverless edge functions:

Personalized Experience: With serverless edge functions, users can personalize content based on preferences, location, and device type.

Video Streaming and Gaming: Processing requests close to users reduces the latency and buffering experienced by streaming video or playing online games.

Security and Authentication: By distributing workload to various edge locations, malicious traffic is checked and filtered before it reaches the central infrastructure.

IOT Devices: Serverless edge computing makes it easy for IOT devices to perform efficiently by ensuring that the infrastructure running the device is close to the users.

8. Challenges and Considerations

Despite the benefits brought by serverless computing, it faces some of the following drawbacks.

• Vendor Lock-in: Most cloud providers provide several services that integrate well with serverless applications. Although it’s possible to use another vendor’s service in your server application, services provided by a single provider are easy to integrate.
• Less Control: Another issue is the lack of control over the server. If there is a problem with the server, such as an outage or hardware problem, you can’t fix it and are left with the cloud provider to fix it.
• Cold Starts: Cold start occurs when a function that has been inactive for a while is invoked and it takes several seconds to execute. This can introduce latency which can affect application performance and also user experience.
• Security: By entrusting your data to a third party and sharing a server with other users, you risk exposing your application and user data if the servers are not configured correctly.

Debugging Complexities: It’s difficult for developers to replicate the production environment locally due to the features of serverless applications, hence making it difficult to test the code locally. Integration tests that test the interaction between frontend and backend code are also difficult to perform when in a serverless environment.

9. Conclusion

• For companies interested in building lightweight applications, serverless computing is the way forward. For applications made up of a large number of services with complex interactions, a hybrid infrastructure made up of virtual machines for large processes is recommended; in this case, the serverless containers are used only for short tasks. Serverless edge functions can also be considered because they minimize latency by processing data locally without putting much workload on the central server.
• I just wanted to let you know that various advancements and developments are still being made to expand the capabilities of serverless computing. For example, a multi-cloud approach can be used to build serverless applications, where one can build a serverless application using services from more than one cloud provider. More developments have also been made to ensure zero cold start of serverless functions, as implemented in Cloudflare Workers.

What’s your experience with serverless computing? How have you leveraged its scalability in your projects? Share your thoughts in the comments!!!!!!

Yashi Verma.