How to Test the Resilience of your Cloud-Native Application

What exactly is a cloud-native application? This is a type of computer software that's made up of tiny, self-contained, and loosely connected services to help exploit the advantages of the cloud computing model and SaaS delivery. They are intended to provide well-known commercial value, such as the ability to quickly incorporate user feedback for continual improvement. In a nutshell, cloud-native app development is a method for accelerating the creation of new apps, optimizing current ones, and connecting them. Its purpose is to offer the apps that people demand at the speed that businesses want.

The cloud-native architectural paradigm has been around for a while. At the heart of cloud-native architecture are coherent, independent functional components that contribute to business agility, scalability, and robustness, resulting in faster time to market, competitive advantages, and cost savings. This paradigm has been actively promoted by a multilingual technological landscape.

Systems developed through the aforementioned mix of design and tech landscape can be extremely difficult to maintain and administrate due to the large number of components and multiple tech frameworks required for their execution. Suboptimal design and engineering approaches raise the complexity and maintenance hazards of such systems enormously.

What Exactly is Resiliency in Cloud Native Application Development?

One such engineering approach that is important to the success or failure of any digital transformation endeavor is "resiliency." As you may be aware, resilience directly contributes to overall system availability via metrics such as Mean Time to Recover (MTTR) and Mean Time Between Failures (MTBF). It's also directly accountable for building or breaking a satisfying user experience.

Resilience is fundamentally a system's capacity to withstand failures. While system failures may eventually show as faults or lack of a system/component, the number of causes that may lead to system collapses in a distributed, cloud-native architecture is extensive.

There is currently a wealth of information available on how to "implement" resilience in cloud-native application. There are also frameworks like chaotic monkey and tools like Gremlin that aid in "testing" application robustness.

However, establishing if a solution is "resilient enough" remains a challenge. How can we know whether our testing covers all the essential and necessary cases? How do we know which failures to generate?

To overcome the aforementioned difficulty, implement the strategy discussed below.

Determine the Scenarios and Architectural Components that Must be Evaluated for Resilience.

This may be accomplished by defining "unique traversal pathways," or the sequence/combination of components on your system that can provide functional situations. These scenarios and their associated components form the foundation for testing.

For instance, your application may support:

• Search/browse the product catalog using a channel application that calls backend microservices, which retrieve data from a persistent data reserve.
• Batch processes/schedulers that run at a predetermined time/frequency.
• Events released on pre-defined subjects and processed by microservices that subscribe to them.
• APIs that may be accessed and used by a wide range of consumer systems.

Identify Failure Locations

Once the scenarios and components have been established, the next step is to determine what may be "wrong" with these components. Let us consider a single microservice with the following characteristics:

• It makes an API available via a gateway.
• It is running on a container framework that supports Kubernetes.
• It connects to a database.
• It connects to a downstream system.
Locating the "failure surfaces" would constitutes the first step towards piecing together this view and coming up with effective remedies.

Identify Failure Causes Across Failure Surfaces

Each failure surface discovered in the previous stage might fail for several reasons. Mapping failure surfaces to possible causes yields the following list:

• Core: The main microservice, as a code unit, may fail owing to out-of-memory concerns, the application server may crash, and so on.
• Microservices pod and node: A health check on the node/pod may fail. The Kubernetes container platform's VM may fail.
• API Gateway: The API Gateway engine may become unusable owing to a lack of threads/memory necessary for request fulfillment.
• Backend system: The backend system may be slow to respond and cause the system to crash.
• Compute/storage/network: The network between the microservice and the backend system (which might be hosted elsewhere) may fail.

Be Ready for "Attack"

The causes and failure surfaces can be combined to form a matrix that enables us to comprehend and arrange the combination with which we must plan for "assaults" on the solution. These, in turn, may now be implemented via chaos testing frameworks.

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