Why Everyone is Talking About Serverless Technology: A Simple Guide to Understanding It

Serverless technology is also known as serverless computing or serverless architecture which is a revolution in the concept of developing and deploying a system. Serverless technology is a cloud computing method of a code execution model in which the cloud provider takes responsibility for the server and allocates the computing resources required on demand. So when people speak about “serverless”, it is much more appropriate to understand that there are no direct or indirect servers of physical or virtual type that the developers and organizations would have to manage. The cloud provider also takes care of all the back-end details – server provisioning, application deployment, management and scaling all happen without any input from the developers – they just write code.
In other forms of cloud computing, developers are required to secure servers and its virtual machines, allocate resources and manage them when needed. This involves activities such as configuration of server environments, capacity management, scaling the resources and patching among others. Serverless technology gets rid of these functions by delegating the entire server management to third-party providers like AWS, Google Cloud oder Azure.
In this model, the cloud provider proactively deploys resources and servers when required and scales them up or down as per the application requirements. This model helps in ensuring that resources are well used and that the cost is controlled since one only pays for what he/she consumed.

1. Function as a Service (FaaS)

1. As a Service (FaaS)

 Function as a Service (FaaS) is the most recognizable form of the serverless computing and is quite often referred to as serverless architecture. FaaS is a system where developers can write and deploy specific functions of code that is triggered to perform specific actions at certain occurrences. Instead of deploying and managing whole applications on servers, developers deploy small independent functions which have one purpose.

 How FaaS Works:

• Event-Driven Execution: Some base FaaS functions are initiated by HTTP requests or file upload, change on database, request based on the scheduled timers, or notification from a queue. In event, the serverless platform deploys enough resources to support the function or application in real time.
• Stateless Functions: The execution of each function is standalone in FaaS; that is why FaaS functions are stateless in nature. Due to their amnesiac nature, these languages that do not have a frame of memory between different executions of a function make the functions to be extremely scalable.
• Automatic Scaling: As it is with FaaS platforms, they adjust the scale of the functions up or down depending on their use. If the traffic increases or several events happen simultaneously, then the service horizontally extends this function. When its demand is low it shrinks to ensure that it uses as few resources as possible.
• Pay-per-Use Pricing Model: Consumers are charged the amount of computational time that has been taken by the func- tions. This is normally on a millisecond basis and cost is incurred only when the function is actually performing. This model cuts out having to impose costs for owning resources that may not be put to use.

 Popular Platforms:

• AWS Lambda: About Amazon’s FaaS offering, the option for coverage of various languages and compatibility with other AWS services.
• Google Cloud Functions: Google’s product launched to facilitate the development of applications with no need for maintaining servers; it is an application that is supported by Google Cloud Services and APIs.
• Azure Functions: Microsoft’s fully managed service for serverless computing supporting multiple languages with integration with Azure services.

2. Backend as a Service (BaaS)

Backend as a Service (BaaS) is another dominant kind of serverless technology. BaaS ismajorly centered on offering developers end-to-end pre-packaged, easy to implement backend services that can be easily incorporated into the application. This model deals with the back end parts of an application for example authentication, databases, storage, notifications and cloud functions among others while giving developers the freedom to create their applications without the need to think of what happens at the back end.

 How BaaS Works:

• BaaS providers give a set of APIs (Application Programming Interfaces) and SDKs (Software Development Kits) wherein the developers can deposit back stop services hassle-freely. Some of them may include; user authentication and authorization, real-time database, file storage, cloud functions, analytics, push notifications, among others.
• The data storage space, database, and existent server structure is governed by the BaaS provider. Such services can be accessed through APIs; all service management, scaling, and maintenance at the server level are provided by the service provider.
• One of the key advantages is that, given the fact that BaaS encompasses all problems related to backend development, developers can concentrate on problems involved in frontend development, customers’ experience and new features.

 Popular Platforms:

• Firebase (by Google): A one-stop BaaS solution for real-time database, cloud functions, analytics and auth and many other facilities.
• AWS Amplify: Amazon’s BaaS solution with APIs, authentication, file storage and others features plus seamless connection to AWS services.
• Backendless: A robust back end as a service that incorporates application logic and data processing, database, real time messaging and users.

Comparison of FaaS and BaaS

Feature	Function as a Service (FaaS)	Backend as a Service (BaaS)
Primary Focus	Executing individual functions or code snippets in response to events.	Providing managed backend services for authentication, databases, etc.
Event-Driven	Yes, functions are triggered by events like HTTP requests, database changes, etc.	Not necessarily; services are available as APIs that the frontend calls.
Scalability	Automatic, based on the number of function invocations.	Automatic, backend services scale based on demand.
State Management	Stateless; each function execution is independent.	Can be stateful; databases and other services maintain state.
Development Approach	Microservice-oriented, focusing on small, independent functions.	Full-stack development, focusing on integrating pre-built backend services.
Cost Model	Pay-per-execution; costs are incurred only when functions run.	Subscription or usage-based pricing depending on the services used.

APIs

Serverless APIs are an implementation of the serverless architecture where API server backend is made of serverless functions. Contrasting with API deployment concept where servers or containers managing to run the backend code, in serverless APIs, the FaaS platforms execute the backend logic in response to the API requests. This approach hides management details of the infrastructures thereby enabling developers to concentrate on developing and deploying their API logic.

 How Serverless APIs Work

 1. API Gateway as an Interface:

• Some of Serverless APIs utilize an API Gateway service (AWS API Gateway, Azure API Manager, or Google Cloud Endpoints). The API Gateway receives clients’ HTTP requests (for example, from web browsers or mobile applications) and forwards them to the relevant serverless functions.
• The API Gateway deals with RESTful or HTTP-based traffic and normally includes features such as request/response transformation, authentication, rate limit, caching and other features.

 2. Integration with Serverless Functions:

•  When a client comes to the API with a request, the API gateway deploys a certain server less function on that endpoint. For instance, a GET request of the form /users may invoke a function to get a list of users from a database, whereas a POST request of form /users may invoke a different function for inserting a new user.
• The said function is used independently and is injected separately to perform certain HTTP methods (GET, POST, PUT, DELETE) and certain paths (/users, /orders).

 3. Automatic Scaling and Load Management

• Since serverless APIs are built using Serverless platforms by design, they gain value from the built-in autoscaling features. When way too many API calls are received, further instances of the function are created by the platform to deal with the pressure.
• On the other hand, when there are low activities in the system, the serverless platform reduces to a minimum in order to avoid situations that causes one to pay for unused resources.

4. Stateless Architecture:

• As with all serverless functions, serverless APIs do not have state and that means, the state of a function at the time of one invocation is not the same as the state of the function at the time of a different invocation. Anything requiring management in a state (for example: session handling, user data storing) is done through other third-party services like databases (for example Amazon DynamoDB, Firebase Firestore) or caches (for example Redis).

5. Secure and Managed API Operations

• It also solves security issues associated with APIs including oauth, API key check, and JSON Web Token (JWT) check. It can also automatically put restrictions on the amount of traffic it forwards to the backend to prevent the backend from being overloaded.
  Many APIs will have ‘baked in’ loggers and monitor apps or functions to give an indication on the execution performance, issues that it may encounter and utilization trends.

6. Cost Efficiency with Pay-Per-Request Pricing:

• SERVERLESS API involves a pay as you go model of computing where charges are based affordable request charges. It operates based on API requests and the amount of time taken by the serverless functions which on balance makes it a cheap way to introduce APIs with fluctuating or unpredictable movements.

History of Serverless Computing

Serverless computing evolved from the initial steps of cloud computing but only started to receive substantial attention from mid-2010s. The term ‘serverless’ is somewhat misleading because the servers are present, but their management is not exposed to developers, therefore all the ‘serverless’ applications are mostly based on a set of connected and pre-configured servers. This changed the architecture from the usual server focus to a new event-based architecture where utilization is billed as a direct consumption and scaling is done automatically.

 Early Foundations and Concepts (Early 2000s – 2010s):

• It is important to mention that serverless computing origins were in the early cloud solutions that include Platform as a Service (PaaS) and Backend as a Service (BaaS). These early models did provide some degree of abstraction of server management which forms the basis for serverless computing.
• Firebase which began in 2011 and was bought over by Google the following year is a BaaS solution where developers where able to fire up back end services without having to worry about servers, a similar concept as the current serverless architectures.

 AWS Lambda (2014):

• AWS Lambda by Amazon Web Services in 2014 can be considered a major event that contributed to bringing serverless computing into mainstream. AWS Lambda brought into the world the Function as a Service (FaaS) which allows developers to execute their code in reaction to events but they do not have to be concerned with the servers. This new approach to the application development and deployment brought dramatic shift in the way that developers approached the construction and growth of applications.

 Adoption by Other Cloud Providers:Adoption by Other Cloud Providers:

• The success of AWS Lambda also inspired the rest of the other major cloud providers to come up with their FaaS platforms further growing the serverless market. In 2016, Microsoft released Azure Functions while Google released Google Cloud Functions both with additional features and functionalities to complement the platforms they belong to. IBM came to the fray in 2017 with IBM Cloud Functions fashioned on Apache OpenWhisk.

Introduction of Open-Source Serverless Frameworks (2015 onwards):

• The serverless landscape was further enriched by the release of the open-source frameworks such as Serverless Framework, (2015) and Apache OpenWhisk. These tools offered the developers the standardized platforms for implementing and orchestrating serverless applications the across different Cloud providers to help standardize and for further support for Multi-Cloud implementations.

Development of Serverless Containers (Post-2017):

• In fact, the serverless model went further than FaaS by introducing the concept of serverless containers. AWS Fargate and Google Cloud Run provided a solution to easily deploy the containerized application without dealing with infrastructure enabling developers to have more freedom in choosing their runtime environments.

Widespread Adoption and Evolution (2016 onwards):

• After the year 2016, serverless computing has started gaining the traction both in terms of available tools and enterprises. Tooling and framework evolutions include AWS COMPOSE/SAM, Terra form and CI/CD composting to name but a few began to emerge. These tools enabled handling of serverless app from a number of perspectives and across different environments.

Emergence of Edge Computing in Serverless (2020 – Present):

• Soon, in 2020, serverless computing expanded its presence in edge computing and had products such as Cloudflare Workers and AWS Lambda@Edge. Such services enabled developers to execute functions near the consumers as well as in the-edge devices, which solved the high latency issues in the case of globally distributed applications.

Source: onlinescientificresearch

Patents in Serverless Technology
Twistlock Ltd. – Protecting Serverless Applications

It is imperative to note that the traditional security approaches cannot be applied due to the fact that serverless architectures do not allow for access to infrastructures, operating systems and networks. To some extent, serverless functions are prone to be exploited for application-level threats such as SQL injections, cross-site scripting and misuse of dependencies of functions. All existing security solutions either reduce performance or are impractical in various serverless environments. The patent (US20240250977A1) brings into discussion a Serverless Security Runtime Environment (SSRE) made out of a Serverless Application Firewall, as well as a Serverless Behavioral Protection Engine. The Serverless Application Firewall examines the input and output of the serverless functions and filters for any kind of malicious or an anomalous data to mitigate an attack at the same time not affecting the execution of serverless functions. The Serverless Behavioral Protection Engine supervises the function’s behavior and ensures compliance with security and protection policies and prevent potential malicious or unauthorized operations

Cisco Technology Inc – improving the efficiency and flexibility of serverless computing environments

The issue solved in this patent is the lack of optimization and resource management of serverless functions particularly in the context of cloud computing where several functions are triggers. It has been observed that in the traditional serverless arrangements, there are problems like delay in warm-up time, ineffective scaling, and resource fluctuation when it comes to managing these functions. Indirect cold start latency which arises when a serverless function has not been in use for some time, the system will take some time in order to set up the runtime environment of the function. Moreover, effective management of the multiple executions at the same time as well as the effective allocation of the resources needed when the resources have to be procured without going overboard to when they take a long time to be used can also be a hurdle in traditional serverless environment. This reoccurring problem is solved in the context of this particular patent (US11016673B2) through a method and system for dynamic serverless function execution that enables a more optimized resource utilization. The approach that is adopted here involves the use of predictive models and real-time monitoring for managing the provisioning and perhaps de-provisioning of resources that are computational in nature. Because the system adapts to the necessary functions’ invocation patterns and manages resources to increase the system’s coverage and reduce the overhead of scaling operations, the system can prevent cold starts. This dynamic management provides a more efficient and economical serverless computing system that enhances the performance, flexibility and efficiency in the usage of resources. It also uses load balancing methodologies and scaling policies for a proper functioning of the loads and resources used to manage the cloud infrastructure.Top of FormBottom of Form

MO Tecnologias LLC – Transaction card system having overdraft capability

The patent US11423365B2 is primarily aimed at improving serverless computing conditions through addressing specific concerns linked to the execution, management, and coordination of serverless functions. The other traditional serverless architectures have some performance issues and weaknesses with regards to general function execution and their resultant resource allocations when under different kind of workloads. Third, the challenges to reduce latency and overhead during function invocation and to handle dependencies are still relevant.
To solve these issues, the patent introduces the concept of a dynamic orchestration model for serverless functions to improve the runtime environment of the function. This is done by using predictive algorithms to control the function invocation and it also has the capability to scale resources in order to deal with interdependencies effectively, which minimizes on the latency of the system. Since the solution automates the resource management and execution flow, it guarantees improved efficiency and reliability and eliminates the need to configure servers for optimum scalability as observed with serverless solutions.

Boomi LP – Serverless Service Architecture for Distributed Workflows

Supervising and organizing serverless architectures especially in API gateways is the primary idea that is developed in the patent US11032160B1 to ease the task of IT specialists. The issue hinges on how multiple API gateways of different forms can be manually set up and managed efficiently when it comes to traffic distribution among these gateways, or when deploying various microservices. Conventional techniques involve significant intervention from IT administrators to manage configurations based on workload and high-level gateway policies.
The solution described in the patent is a serverless and flexible scaling system for API gateway management that is able to convert high-level policies of the gateway into configurations for various types of the gateway. It manages API traffic in real time, adapts settings on its own, and distributes the load between different gateways without human interventions. By adopting this approach, API product managers can effectively manage APIs and microservices in different environments and increase the adaptability, extensibility, and performance of the technology with less demand for IT skills.Top of Form

DigitalOcean LLC – Efficient Deployment and Execution of Serverless Functions Using Combinatorial URLs

The patent addresses the challenge of deploying and executing serverless functions efficiently in cloud environments. Traditional serverless systems require complex management of function source code and computational resources, often leading to inefficiencies in execution and security vulnerabilities. The difficulty lies in managing isolated execution environments while ensuring scalability, security, and ease of use for developers, especially when dealing with dynamic workloads and diverse API types. The proposed solution is a serverless function execution system that uses combinatorial URLs to link function source code with computational resources, allowing functions to be deployed as GraphQL or RESTful APIs without additional configuration steps. This system leverages function isolation techniques to prevent unauthorized access and dynamically manages the creation and destruction of isolated units (e.g., containers, micro VMs) to optimize resource usage. The system supports automatic scaling and provides enhanced security features, such as hidden functions and encrypted access, to protect function source code and data from malicious actors.