Fault Tolerance for Serverless Computing: Ensuring Reliability in a Dynamic Environment

Serverless computing and Function-as-a-Service (FaaS) are one of the key drivers of operational efficiency, adaptability and scalability in software development. However, there are still gaps to be addressed, particularly in fault tolerance, which needs to be enhanced for this architecture to reach its full potential. In this article, we will define serverless computing and its benefits, outline the key challenges it poses, and delve into the concept of fault tolerance. We will discuss two popular design patterns used in software development and present best practices for achieving higher stability and maintenance of serverless architecture.

What is serverless computing?

Serverless computing is an approach to application development that allows developers to execute code in response to events without provisioning or managing servers. This means organizations no longer need to allocate resources, scale, or maintain servers for running applications, databases, and storage systems. Some examples of common servers include AWS Lambda, Google Cloud Functions, and Azure Functions. Simply put, serverless architecture is an event-driven and request-based technological solution. These frameworks are particularly useful in a dynamic environment, under tight deadlines, and for tasks that require substantial resources and efforts. Tech giants like Google, Microsoft, IBM, and Amazon offer their clients the ability to migrate on-premises business processes to achieve operational efficiency on flagship serverless platforms such as AWS Lambda and Azure Functions.

Why use serverless architecture?

The fact that developers can focus on their core product without worrying about server management or runtime environments is one of the biggest advantages of serverless architectures. Not only does it enable developers to create robust products with high reliability and scalability, but it also saves tons of time and effort.

What are the benefits of serverless computing?

The following advantages makes serverless computing a highly attractive option for modern application development and deployment in the cloud. Serverless computing offers several advantages, listed below.

Enhanced scalability

The flexibility of serverless computing allows computing resources and functionalities to scale up precisely when needed. Conversely, during off-peak times, the working environment contracts, optimizing resource use.

Cost efficiency

Cost minimization is perhaps the most striking factor. Pay-as-you-go billing helps avoid unnecessary expenses during application development and deployment.

Easier-to-use deployment environment

The built-in flexibility of serverless architecture guarantees straightforward automation for most technological processes, which is extremely convenient for developers.

Boosted stability

Serverless architecture offloads the bulk of data management and infrastructure responsibilities to the cloud service provider. This means teams can avoid a significant headache associated with infrastructure maintenance, orchestration, and data distribution.

Reduced latency

By deploying infrastructure on cloud servers, companies can position their resources on the servers closest to end users which ensures connectivity, compatibility of data without delays and system downtimes.

What challenges are presented by serverless infrastructure failures?

While serverless computing offers many benefits, it still has scalability limitations. For example, AWS Lambda can handle increased concurrency only up to a certain limit per. If you suddenly generate tens of thousands of concurrent requests, it will throttle. Failure in serverless architectures typically occur due to:

Timeout errors
Increased latency
Concurrency limitations

These cases can escalate into larger failures due to dependencies within cloud architectures.

What are some examples of serverless architecture applications?

There are numerous types of applications that can be entirely developed in the cloud. Let's focus on specific services offered by cloud providers as serverless functions or FaaS. Below are some of the most popular examples.

Rapid document conversion

Serverless functions can be used to quickly convert documents between different formats, making it an efficient solution for handling large volumes of document processing.

Webpage rendering

Serverless architecture can efficiently render webpages on the fly, allowing for dynamic and scalable web applications without the need to manage servers.

Automated backup

Serverless functions can help in automating the backup process. They ensure that data is continuously and securely backed up without the intervention from developers.

Real-time data processing

Serverless architecture is ideal for real-time data processing tasks, such as streaming data analysis, event detection, and log processing, providing scalable and responsive solutions.

What is fault tolerance and how is it applicable to serverless applications?

Fault tolerance is the capability of a system—be it a computer, network, or cloud infrastructure—to maintain continuous operation even when one or more of its components experience failure. The primary goal of developing a fault-tolerant system is to avoid interruptions caused by any single point of failure. This ensures the high availability and continuous operation of essential applications or systems, thereby supporting business continuity. While it might seem like such issues are unavoidable, there are strategies to avoid, or at least mitigate, these failures. To achieve fault tolerance in serverless computing, strategies and design patterns can be employed. Below are two examples of common design patterns.

Circuit breaker

To put it simply, the circuit breaker pattern stops a service from trying to contact another service repeatedly after it has failed or timed out several times. It also monitors the failed service and identifies when it is working again. This pattern prevents cascading failures and enables the system to respond faster by reducing long wait times. The example below uses the AWS Step Functions, AWS Lambda, and Amazon DynamoDB to implement the circuit breaker pattern. Flowchart depicting a circuit breaker pattern for Amazon DynamoDB. The process ensures reliability with steps like getting circuit status, executing a Lambda function in serverless computing, and updating circuit status based on success or failure to maintain fault tolerance.

Flowchart depicting a circuit breaker pattern for Amazon DynamoDB. The process ensures reliability with steps like getting circuit status, executing a Lambda function in serverless computing, and updating circuit status based on success or failure to maintain fault tolerance.

Source: AWS

The bulkhead pattern

The bulkhead pattern takes its name from naval engineering, where ships have internal compartments to prevent water from flooding the entire ship if the hull is damaged. In software development, this pattern isolates resources and dependencies to prevent widespread failure. This makes systems more available and fault-tolerant by containing failures, making them easier to recover from and reducing the impact of noisy neighbors. Below is an example of a fault tolerant architecture with a bulkhead pattern on AWS App Mesh, a service mesh that makes it easy for services to communicate. It standardizes how services communicate, giving visibility and ensuring high-availability for the applications. Diagram illustrating a load balancer connected to a virtual gateway, virtual service, and virtual router which ensures reliability and directs traffic to virtual nodes and price containers in an EKS deployment.

Diagram illustrating a load balancer connected to a virtual gateway, virtual service, and virtual router which ensures reliability and directs traffic to virtual nodes and price containers in an EKS deployment.

Source: AWS

What are best practices for fault tolerance in serverless computing?

Apart from the various types of design patterns to boost fault tolerance, the practices below play a vital role in maintaining the stability and performance of serverless applications.

Regular testing and chaos engineering

Tools like Gremlin, LitmusChaos, and Chaos Mesh make systems more resilient by simulating failures and testing responses. They also help in identifying weaknesses and driving improvements in reliability and downtime reduction.

Load balancing and failover strategies

Load balancers act like managers, directing traffic to the healthiest instances, while failover strategies ensure that if one instance fails, another steps in immediately. This guarantees that your system remains available and performs optimally, even in the face of failures.

Regular health checks

Automated health checks can help you spot issues at earlier stages, enabling you to fix anything before it becomes serious.

Using managed services with built-in cloud computing fault tolerance

AWS Lambda and Azure Functions are equipped with fault tolerance features, meaning you won’t have to create your own custom solutions.

Conclusion

Fault tolerance is a cornerstone of an efficient software development environment. Failure to implement best practices and patterns can lead to situations where your organization is held back by reduced capacity and limited scalability. From innovative startups to large corporations, Kanda is an expert in serverless computing and can help your business with AWS development, management, optimization, automation, and deployment, all while staying relevant to your specific business needs and budgets. We understand the importance of building reliable infrastructure with high fault tolerance and adaptability. Contact Kanda today to take full advantage of AWS tools to improve your operational efficiency.