News

Cilium 1.4: Multi-Cluster Service Routing, DNS Authorization

We are excited to announce the Cilium 1.4 release. The release introduces several new features as well as optimization and scalability work. The highlights include the addition of global services to provide Kubernetes service routing across multiple clusters, DNS request/response aware authorization and visibility, transparent encryption (beta), IPVLAN support for better performance and latency (beta), integration with Flannel, GKE on COS support, AWS metadata based policy enforcement (alpha) as well as significant efforts into optimizing memory and CPU usage.

Introducing Ludwig, a Code-Free Deep Learning Toolbox

Over the last decade, deep learning models have proven highly effective at performing a wide variety of machine learning tasks in vision, speech, and language. At Uber we are using these models for a variety of tasks, including customer support, object detection, improving maps, streamlining chat communications, forecasting, and preventing fraud. Many open source libraries, including TensorFlow, PyTorch, CNTK, MXNET, and Chainer, among others, have implemented the building blocks needed to build such models, allowing for faster and less error-prone development.

Kubernetes network deep dive: Did you make the right choice?

China’s CRISPR twins might have had their brains inadvertently enhanced

Kubernetes Metrics and Monitoring

This post explores the current state of metrics and monitoring in Kubernetes by walking through the gradual thought process that I experienced when learning this topic. Kubernetes needs some metrics for it’s basic out-of-the-box functionality, like autoscaling and scheduling. This is regardless of any monitoring solution you may want for the purpose of troubleshooting and alerting.

Kubernetes Operations: Prioritize Workload in Overcommitted Clusters

One of the benefits in adopting a system like Kubernetes is facilitating burst-able and scalable workload. Horizontal application scaling involves adding or removing instances of an application to match demand. Kubernetes Horizontal Pod Autoscaler enables automated pod scaling based on demand.

Use Istio traffic mirroring for quicker debugging

Often when an error occurs, especially in production, one needs to debug the application to create a fix. Unfortunately the input that created the issue is gone. And the test data on file does not trigger the error (otherwise it would have been fixed before delivery).

Kubernetes Ingress Controllers: How to choose the right one: Part 1

When AWS Autoscale Doesn’t

The premise behind autoscaling in AWS is simple: you can maximize your ability to handle load spikes and minimize costs if you automatically scale your application out based on metrics like CPU or memory utilization. If you need 100 Docker containers to support your load during the day but only 10 when load is lower at night, running 100 containers at all times means that you’re using 900% more capacity than you need every night. With a constant container count, you’re either spending more money than you need to most of the time or your service will likely fall over during a load spike.

Kubernetes at CERN: Use Cases, Integration and Challenges

Kubernetes at CERN: Use Cases, Integration and Challenges.