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Gremlin

How to fix the root cause of a failed reliability test

You’re well on your way to becoming more reliable. You’ve added your services, found and fixed some Detected Risks, and run your first set of reliability tests. However, some of your tests returned as “Failed.” Not to worry: this isn’t a reflection of you or your engineering skills but rather an opportunity to learn more about how your systems work and, more importantly, how to make them more resilient.

Maximizing your reliability on AWS

Cloud providers like AWS excel at creating reliable platforms for developers to build on. But while the platforms may be rock-solid, this doesn’t guarantee your applications will be too. It’s the provider’s job to offer stable infrastructure, but you’re still on the hook for making your workloads resilient, recoverable, and fault-tolerant. There’s only one problem: cloud platforms are essentially black boxes.

Manage your reliability work more easily with Gremlin's newest features

Reliability testing is ongoing work, and tracking that work can be difficult in large organizations. Engineers run one-off experiments, scheduled Scenarios run in the background, and, for more mature teams, CI/CD workflows fire off automated tests on demand. According to our own product metrics, teams run an average of 200 to 500 tests each day! With so much happening, it’s hard to keep track of everything going on in Gremlin—until now.

Gremlin's 2024 year-end Release Roundup

It’s been a busy year at Gremlin! We released two new experiments, added an entirely new onboarding process and features for AWS users, added a brand new Test Suite and Detected Risks, and made many UI improvements to our web app. We beefed up our agents with more enterprise capabilities, including support for large Kubernetes clusters and systems with over 64 CPUs, improved experiment behaviors, improved dependency detection, and per-team Private Network Integrations.

Why Gremlin: Today's complex applications need a different approach to reliability

Cloud-based distributed applications have changed how we need to approach reliability and resiliency. How do you make your applications reliable? Here’s Gremlin CEO Josh Leslie to tell you how. Today’s dynamic applications are too complex and constantly changing for humans to wrap their heads around. This means the reliability approaches that worked ten years ago simply won’t be enough. As a technology company (and these days, every company is a technology company), you need to take a different, programmatic approach to testing and improving the reliability of your applications.

Test for the common failures that cause 80% of outages with Gremlin

80% of failures at the infrastructure layer come from the same core gaps in reliability. Jeff Nickoloff, Gremlin Principal Engineer, goes over how Reliability Management test suites help improve reliability across your organization. Are you waiting for the other reliability shoe to drop and hoping that you actually fixed core resilience issues? Or do you know for sure that you’re resilient to common reliability issues?

Release Roundup November 2024: Reliability in the serverless and AI era

2024 is coming to a close, and while many teams are slowing down in preparation for the holidays, we’ve been cooking up tons of new features. We’ve extended our platform support to the Istio service mesh, added a brand new experiment type for testing artificial intelligence (AI) and large language model (LLM) workloads, and made it easier to onboard Kubernetes clusters. We’ve also made our Linux and Windows agents more robust and performant.

Now in private beta: Gremlin Service Mesh Extension

Service meshes like Istio have become an essential way to securely and reliably distribute network traffic, especially with ephemeral, service-based architectures such as Kubernetes. However, their constantly shifting nature can interfere with targeting specific services for resilience tests. Infrastructure-based testing is designed to target specific IP addresses, allowing precision testing of applications, VMs, and nodes.

Reliable AI models, simulations, and more with Gremlin's GPU experiment

Note This blog uses “GPU” to refer to the entire processing circuit, including the GPU processor, video memory, and other supporting hardware. ‍ Artificial Intelligence (AI) has become one of the biggest tech trends in years. From generating full movies to updating its own code, AI is performing tasks that were once science fiction.

Integrating Gremlin with your observability tools

Part of the Gremlin Office Hours series: A monthly deep dive with Gremlin experts. To get the most value out of Chaos Engineering and reliability testing, you need a way to observe your service’s behavior. Observability tools offer insight into how your systems are performing, but observability on its own isn’t enough. You need a way to monitor your systems while testing their reliability so you can determine whether your service passed or failed a test.