Chaos Mesh Introducing Chaos in Kubernetes

Chaos Mesh:
Introducing Chaos in
Kubernetes
Shubham Chaubey
Software Consultant
DevOps Competency

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1. What is Chaos Engineering?
2. Principles of Chaos Engineering
3. Introduction to Chaos Mesh
4. Key Features of Chaos Mesh
5. How Chaos Mesh Works
6. Types of Chaos Experiments
7. Demo

What is Chaos Engineering?
 Chaos Engineering is a disciplined approach to identifying failures before they become outages.
 In Chaos engineering, we simulate real-world failures to understand system behavior and how the
system reacts to it.
 Proactively identifies weaknesses and improves reliability.
 Chaos Engineering lets you compare what you think will happen to what actually happens in your
systems.
 You literally “break things on purpose” to learn how to build more resilient systems.

Principles of Chaos Engineering
 Define Steady State: Determine what normal operation looks like for your system (e.g., response
times, error rates).
 Hypothesize Impact: Predict how introducing certain failures will impact the steady state (e.g.,
will the system still respond quickly?).
 Introduce Realistic Faults: Inject controlled failures that mimic real-world issues (e.g., server
crashes, network latency).
 Observe and Learn: Monitor the system to see if it behaves as expected and identify areas for
improvement.

Introduction to Chaos Mesh
 Chaos Mesh is an open-source cloud-native Chaos Engineering platform.
 It is designed to orchestrate chaos experiments specifically in Kubernetes environment.
 It offers various types of fault simulation and has an enormous capability to orchestrate fault
scenarios.
 Helps in identifying system vulnerabilities and improving resilience.

Key Features of Chaos Mesh
 Supports various types of faults including:
- NetworkChaos
- HTTPChaos
- PodChaos
- StressChaos
- And more...
 Chaos Mesh provides the Chaos Dashboard component for visualized operations, which greatly
simplifies Chaos experiments.
 Allows you to run a single experiment or create a series of experiments termed as a workflow
 Integration with other cloud-native tools.

Types of Chaos Experiments
 PodChaos: simulates Pod failures, such as Pod
node restart, Pod's persistent unavailablility, and
certain container failures in a specific Pod.
 NetworkChaos: simulates network failures, such
as network latency, packet loss, packet disorder,
and network partitions.
 DNSChaos: simulates DNS failures, such as the
parsing failure of DNS domain name and the wrong
IP address returned.
 HTTPChaos: simulates HTTP communication
failures, such as HTTP communication latency.
 StressChaos: simulates CPU race or memory
race.
 IOChaos: simulates the I/O failure of an
application file, such as I/O delays, read and write
failures.
 TimeChaos: simulates the time jump exception.
 KernelChaos: simulates kernel failures, such as
an exception of the application memory allocation.
 AWSChaos: simulates AWS platform failures, such
as the AWS node restart.
 GCPChaos: simulates GCP platform failures, such
as the GCP node restart.
 JVMChaos: simulates JVM application failures,
such as the function call delay.

Chaos Mesh Introducing Chaos in Kubernetes

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