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At Facebook, we think that artificial intelligence that learns in new, more efficient ways – much like humans do – can play an important role in bringing people together. That core belief helps drive our AI strategy, focusing our investments in long-term research related to systems that learn using real-world data, inspiring our engineers to share cutting-edge tools and platforms with the wider AI community, and ultimately demonstrating new ways to use the technology to benefit the world.

I’m sure many of you have heard of the “Death Star Security” model—the hardening of the perimeter, without much attention paid to the inner core—and while this is generally considered bad form in the current cloud native landscape, there is still many things that do need to be implemented at edge in order to provide both operational and business logic support. One of these things is rate limiting. Modern applications and APIs can experience a burst of traffic over a short time period, for both good and bad reasons, but this needs to be managed well if your business model relies upon the successful completion of requests by paying customers.

We are interested in open-endedness at Uber AI Labs because it offers the potential for generating a diverse and ever-expanding curriculum for machine learning entirely on its own. Having vast amounts of data often fuels success in machine learning, and we are thus working to create algorithms that generate their own training data in limitless quantities. In the normal practice of machine learning, the researcher identifies a particular problem (for example, a classification problem like ImageNet or a video game like Montezuma’s Revenge) and then focuses on finding or designing an algorithm to achieve top performance.

In a previous post, we saw how to leverage Istio Multicluster to deploy an application (bookinfo) on multiple Red Hat OpenShift clusters and apply mesh policies on all of the deployed services. We also saw that the deployment process was relatively complex. In this post we are going to see how Federation V2 can help simplify the process of deploying an application to multiple clusters. Federation V2, as the name suggests, is the evolution of the Kubernetes Federation initiative, after the project was rebooted around the beginning of 2018.

Running production quality databases in Kubernetes can be quite a hassle. But KubeDB promises to solve all your problems. Let’s have a quick look at this framework. You can count on KubeDB to solve your problems when running production quality databases in Kubernetes. KubeDB is a framework for writing operators for any database that support certain requirements. Namely: Create a database declaratively using CRD. Take one-off backups or period backups to various cloud stores, eg,S3, GCS, etc.

I started to compile a list of public failure/horror stories related to Kubernetes. It should make it easier for people tasked with operations to find outage reports to learn from. Since we started with Kubernetes at Zalando in 2016, we collected many internal postmortems. Docker bugs (daemon unresponsive, process stuck in pipe wait, ..) were a major pain point in the beginning, but Docker itself has become more mature and did not bite us recently.

For most of the past 50 years, the areal density of hard disks—a measure of how many bits of data that engineers can squeeze into a given area—increased by an average of nearly 40 percent each year. Lately, though, that rate has slowed to around 10 percent. Everyone who works on magnetic storage is well aware of this problem, but only in the past year or so have executives from Seagate Technology and Western Digital, the leading manufacturers of hard drives, very publicly split on how to solve it.

If you’ve been following the evolution of the Kubernetes templates for Jaeger, you might have noticed an important change recently: the Jaeger Agent is not being deployed as a DaemonSet anymore. Instead, instructions are now being provided on how to deploy it as a “Sidecar”. The Agent component was developed to act as a “buffer” between the tracer and the collector. This buffer should sit very close to the tracer, usually on localhost.

At Namely we’ve been running with Istio for a year now. Yes, that’s pretty much when it first came out. We had a major performance regression with a Kubernetes cluster, we wanted distributed tracing, and used Istio to bootstrap Jaeger to investigate. We immediately saw the potential of a service mesh as it relates to our infrastructure and decided to make an investment in the tool. It hasn’t always been the smoothest ride, but we have learned a ton about how it works and how to operate it.

This article uses Istio’s official bookinfo example to explain how Envoy performs routing forwarding after the traffic entering the Pod and forwarded to Envoy sidecar by iptables, detailing the inbound and outbound processing. For a detailed analysis of traffic interception, see Understanding Envoy Sidecar Proxy Injection and Traffic Interception in Istio Service Mesh. Source: medium.com