3 Models for Consuming Kubernetes in the Cloud

A Brief History on the Growth Explosion of Containers

kubernetes

A couple of years ago, Linux containers began to gain popularity among devs and ops folks alike. It was a win win scenario as the benefits of containers to both dev and ops are clear. As time moved along and the adoption of containers in production workloads exceeded many people’s expectations, the world needed a way to manage container lifecycles at scale.

Enter Kubernetes, while it is not the only proven open source container manager out there (see Docker Swarm & Mesos,) it certainly has the majority share of voice in the community. Originally a Google project that was a core component of their internal Borg system, it has since been open sourced and embraced by companies like Microsoft and Red Hat (OpenShift is powered by Kubernetes.) Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications.

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3 Application Architectures for Successful Disaster Recovery in the Cloud

Over the past few years, we’ve written several blog posts about various disaster recovery methods for your applications running atop of Cloud-A’s infrastructure. If you take a look at these various articles, which highlight software and tools to help you achieve your disaster recovery requirements, you’ll notice that there there is more than one way to skin this cat.

The method you use to backup and recover your applications and data should vary depending on the technical requirements of a given application and/or data store and your organization’s tolerance for downtime for that app or data store.

Let’s dig into three conceptual models for disaster recovery on Cloud-A. We’ve ranked these methods 1-5 by data resiliency, time to recover and cost.

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