Chaos Engineering for a More Secure Kubernetes

Chaos Engineering and Kubernetes for Security

Netflix, Amazon, Google, Facebook, and a host of other companies have adopted chaos engineering, which encourages designing systems to proactively ward off potential issues through testing and the anticipation of failure. When it comes to container orchestration tools like Kubernetes, chaos engineering is a vital tactic for enhancing security.

Application ecosystems are becoming more and more diverse as movement from monoliths to microservices has accelerated. The modularity that results from this progression has made it easier to manage individual applications. However, the increased number of moving parts in microservices-based architectures—another side effect of this shift—has raised new security concerns. Environments have many more points to monitor, complicating the oversight process. Furthermore, there’s no clear-cut debugging approach that spans all services.

To overcome many of these challenges, companies often deploy a Kubernetes layer underneath their containerized applications. This lets teams offload cumbersome maintenance tasks, including health checks and process killing. Kubernetes is often the easiest way to manage cloud-based application environments. In addition to controlling service-to-service comms, companies can add service meshes in order to boost metrics collection, load management, and tracing. For many organizations, however, these additions to the environment are still not enough. Poor configurations  can expose sensitive information.

This article will examine the tenets of chaos engineering. Then, with an understanding of how and why it works, we’ll look at how chaos engineering can be used to secure Kubernetes environments.

What Is Chaos Engineering?

At its core, chaos engineering involves the deliberate injection of havoc into points within your service’s back end, front end, application code, or overall infrastructure. After observing how services react to stress testing, you can patch any resulting weaknesses to prevent further failures down the line. Chaos engineering tests aren’t arbitrary; instead, their design accounts for real challenges that could arise at the drop of a hat.

No system is bug-free or immune to problems, so chaos engineering does not create a perfect environment. It does help you easily identify the low hanging fruit, however. From there, you can target peskier issues. Problems can increase in severity as your services scale, so fixing them before they rear their ugly heads in production is essential.

Thanks to their complexity, distributed systems are great testbeds for chaos engineering. Because a Kubernetes system is the sum of numerous clustered services, it’s vital that all of them be fortified. The links between these services may also be susceptible to external attacks. Preserving universal stability and security isn’t easy, but a little chaos can stave off a lot of turbulence.

Chaos Engineering Key Principles

Kubernetes cluster best practices are founded on stability and security, and so are chaos engineering’s guidelines. The key principles described below outline the functional and philosophical drivers of chaos testing

Build Hypotheses Around Your System’s “Steady State”

By gathering and analyzing data that reflect how your Kubernetes system operates, you can define your baseline state or steady state. The following metrics play a central role here:

  • Throughput
  • Error rates
  • Latency
  • Traffic rates

You can use this data to determine how your Kubernetes setup normally behaves. Chaos testing will reveal how these metrics change, putting the effects of your experimentation on full display. Since you know how your network should to work, chaos engineering can verify that your system is functional. This testing is not meant to determine how your setup works.

Simulate Diverse, Realistic Events

Chaos testing should reflect the varied severity and frequency of challenges that real world development environments face. Experimentation should introduce a wide range of stressors to your Kubernetes clusters. Network activity and traffic spikes can occur randomly throughout your service’s uptime. Focus on designing chaos events that will impact your steady state the same way in simulation as they would in the wild. Both failure and non-failure events should be included.

Don’t Discount Production Testing

It’s a given that your system will behave differently from day to day during its operation, as a result of fluctuations in traffic and resource utilization. Capturing requests throughout your Kubernetes ecosystem relies on analysis in production. How your system performs in the wake of chaos is an excellent barometer of its overall health. Accordingly, live chaos injection is an effective way to uncover weaknesses. Better still is automated testing that occurs periodically and allows your team to review activity spikes, resource allocations, and storage integrity in real time.

Shrink the Blast Radius

Production experimentation isn’t without its faults. Depending on the scope of your chaos testing, some customers may see adversely effects from your efforts—a necessary trade-off in the pursuit of better understanding and regulation of your environment. That said, good chaos engineering practices work to minimize user-facing impacts. Containment is crucial. Ideally, your customers will experience temporary blips, not major disruptions. 

Interestingly, the way we shrink the effects of a test is to gradually expand it until the faults are uncovered. Some problems only exist when you’re dealing with large numbers of containers. You’ll likely miss some issues if the scope of your experiment is too narrow. 

Linking Kubernetes and Chaos Engineering

Kubernetes systems operate around a collection of clusters, which contain configurations, allocated resources, and applications. While these clusters are beneficial for packaging resources and applications efficiently, they introduce more complexity to your environment. These variables can impact performance and reliability.

Thankfully, both the Kubernetes layer and its service mesh are fairly adept at steadying the ship automatically through increased administrative control. We discussed how tracing can make it easy to track failure points throughout your system. This closer look marries well with chaos engineering, allowing you to really drill down into the minutiae.

Kubernetes and chaos engineering focus on minimizing your services’ downtime. This stability is key to your success and profitability. Amazon lost an estimated $100 million when its platform went down for a mere 63 minutes back in 2018. This incident made the importance of continuous uptime abundantly clear. Streaming services and commerce sites are particularly impacted by outages; however, downtime has significant impacts on customers in other industries as well. 

Additionally, metrics and databases contain personal information and company-specific insights that should remain private. Following chaos engineering best practices helps to bolster overall security and privacy.

Applying Chaos Engineering to Kubernetes

When experimenting with chaos, you’ll want to focus on the most common weak points with Kubernetes implementations. Sam Bocetta, in a popular Container Journal article, recommends taking these five steps when testing your deployments:

1. Simulate network failure in Kubernetes:

Kubernetes clusters are hosted on cloud solutions like Amazon ECS/AWS/EKS, Digital Ocean, and Google Cloud—all of which are complicated platforms in themselves. Your applications must be resilient, and they must recover well, even when IP addresses change or networks are faulty. You can simulate these conditions by disabling network nodes and observing the results.

2. Maximize resource consumption:

It’s easy to individualize and customize application containers on the resource side. But, they still rely on backend hardware. If you simulate CPU “exhaustion” and resulting server downtime, you can see how your containers react during that period and gain insight into automatically reallocating resources. In response, you can establish tailored health checks that alert you to future resource issues.

3. Account for global traffic:

Local traffic alone won’t give you an accurate picture of system performance. By simulating worldwide connections, you can observe the Kubernetes load balancing process. Varied traffic can overwhelm your ecosystem. Integrating OpenVPN is a great way to test the use of global servers while allowing you to restrict who can access your services. Locking down configuration to certain groups will help keep hackers at bay.

4. Simulate internal DNS failure:

Web requests that route through DNS, and Kubernetes services must be able to interpret incoming-outgoing connections. Chaos testing by blocking DNS internally can reveal how Kubernetes pods react and how traffic changes. This process highlights the importance of DNS logging and makes it easier to nail down pesky future bugs.

5. Maximize storage:

Your Kubernetes layer relies on either local or cloud storage. While clusters are good at provisioning memory, resource balancing can fail. Furthermore, drive crashes can cause load issues. A chaos test could introduce drive latency, tricking the system into thinking your cloud or physical storage is unreliable. Your future Kubernetes system may need to add volume provisioning on the fly to account for these issues.

Monitor Your Chaos Testing with

The above tests can help identify major issues within your Kubernetes deployment. Eliminating  these potential showstoppers will do wonders for establishing the security and stability of your environment. Testing can help you and your system learn from each failure while illustrating why redundancy and failure mechanisms are essential. These insurance policies will help your Kubernetes ecosystem triumph over unforeseen issues.

Metrics lie at the heart of chaos engineering. None of your testing matters if you can’t interpret your results in real time. Effective data collection and visualization are essential. Logz makes it easy to monitor your Kubernetes solution, both in its steady state and while it’s undergoing chaos testing. compiles your data into one unified dashboard that integrates open source or formerly open source tools for visualizing logs and metrics data. Our AI allows for quick troubleshooting and automation of the problem solving process. Best of all, most security issues can be patched before they impact users.

Couple all that with customized alerting, and you have one potent platform for chaos engineering. Kubernetes doesn’t have to be complicated. Let us help you to simplify your Kubernetes security strategy today.

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