In this article we will discuss the benefits containers bring to business continuance, reveal concepts for applying containers to disaster recovery and of course show disaster recovery of a live database between production and DR OpenShift environments. Business continuance of course is all about maintaining critical business functions, during and after a disaster has occurred. Business continuance defines two main criteria: recovery point objective (RPO) and recovery time objective (RTO). RPO amounts to how much data loss is tolerable and RTO how quickly services can be restored when a disaster occurs. Disaster recovery outlines the processes as well as technology for how an organization responds to a disaster. Disaster recovery can be viewed as the implementation of RPO and RTO. Most organizations today have DR capabilities but there many challenges.
- Cost – DR usually is at least doubles the price.
- Efficiency – DR requires regular testing and in the event of a disaster, resources must be available. This leads to idle resources for 99.9% of the time.
- Complexity – Updating applications is complex enough but DR requires a complete redeployment where the DR side almost never mirrors production due to cost.
- Outdated – Business continuance only deals with one aspect, disaster recovery but as mentioned cloud-native applications are active/active so to be effective today, business continuance architectures must cover DR and multi-site.
- Slow – DR often is not 100% automated and recovery is often dependent on manual procedures that may not be up to date or even tested with the latest application deployment.
I would take these challenges even further and suggest that for many organizations business continuance and DR is nothing more than a false safety net. It costs a fortune and in the event of a true disaster probably won’t be able to deliver RPO and RTO for all critical applications. How could it when DR is not part of the continuous deployment pipeline and being tested with each application update? How could it with the level of complexity and scale that exists today and not 100% automation?
In this article we will explore why you should consider tackling IaaS and PaaS together. Many organizations gave up on OpenStack during it’s hype phase, but in my view it is time to reconsider the IaaS strategy. Two main factors are really pushing a re-emergence of interest in OpenStack and that is containers and cloud.
Containers require very flexible, software-defined infrastructure and are changing the application landscape fast. Remember when we had the discussions about pets vs cattle? The issue with OpenStack during it’s hype phase was that the workloads simply didn’t exist within most organizations, but now containers are changing that, from a platform perspective. Containers need to be orchestrated and the industry has settled in on Kubernetes for that purpose. In order to run Kubernetes you need quite a lot of flexibility at scale on the infrastructure level. You must be able to provide solid Software Defined Networking, Compute, Storage, Load Balancing, DNS, Authentication, Orchestration, basically everything and do so at a click of the button. Yeah we can all do that, right.
If we think about IT, there are two types of personas. Those that feel IT is generic, 80% is good enough and for them, it is a light switch: on or off. This persona has no reason whatsoever to deal with IaaS and should just go to the public cloud, if not already there. In other words, OpenStack makes no sense. The other persona feel IT adds compelling value to their business and going beyond 80% provides them with distinct business advantages. Anyone can go to public cloud but if you can turn IT into a competitive advantage then there may actually be a purpose for it. Unfortunately with the way many organizations go about IT today, it is not really viable, unless something dramatic happens. This brings me back to OpenStack. It is the only way an organization can provide the capabilities a public cloud offers while also matching price, performance and providing a competitive advantage. If we cannot achieve the flexibility of public cloud, the consumption model, the cost effectiveness and provide compelling business advantage then we ought to just give up right?
I also find it interesting that some organizations, even those that started in the public cloud are starting to see value in build-your-own. Dropbox for example, originally started using AWS and S3. Over last few years they built their own object storage solution, one that provided more value and saved 75 million over two years. They also did so with a fairly small team. I certainly am not advocating for doing everything yourself, I am just saying that we need to make a decision, does IT provide compelling business value? Can you do it for your business, better than the generic level playing field known as public cloud? If so, you really ought to be looking into OpenStack and using momentum behind containers to bring about real change.
In this article we will focus on security and vulnerability strategies for scanning container images. I know, in the past security was always viewed upon as an impedance to the speed of production but hopefully these days are behind us. Having a security breach, as you probably know, is one of the most costly things an organization can endure. It takes years to build up a reputation and only seconds to tear it down completely.
I still see today, many organizations ignoring container images completely because it is often misunderstood. Exactly what is inside a container image? Who should be responsible for it? How does it map to what we have done on servers? Security teams often don’t understand containers or even know what questions to ask. We need to help them and it is our duty to do so. Unfortunately there are not very many tools that can help in broad sense. Containers are new and evolving at breakneck speed. That coupled with the fact that security can negatively impact the speed of a DevOps team (if not done right), it is no wonder we are at square one, in many cases.
Before we dive into more detail, let us review important security aspects of containers.
- Containers can have various packaging formats, Docker is the most popular today
- Containers are immutable and as such are image based
- Container are never updated, any change always results in a new container
- Container images consist of layers (base, runtime, application)
- Container images require shared responsibility between dev and ops
- Containers don’t contain, they are in fact, just processes
For more information I recommend reading about the 10 layers of container security.
As this will be the last article of 2017 I wanted to do something different and get away from my typical how-to guides (rest assured I will continue doing them in 2018). Over the past year, I have engaged in a lot of conversation with many large organizations looking to adopt or increase their container footprint. In this article I will share my thoughts on what I have learned from those discussions. We will discuss the impact of containers in large IT organizations. Understand the difference between container technology and container platform. Look into the integration points a container platform has into the existing IT landscape and finally discuss high-level architectural design ideas.
This article should serve as a good starting point for IT organizations trying to understand how to go about adopting container technology in their organization.
Egress traffic is traffic going from OpenShift pods to external systems, outside of OpenShift. There are two main options for enabling egress traffic. Allow access to external systems from OpenShift physical node IPs or use egress router. In enterprise environments egress routers are often preferred. They allow granular access from a specific pod, group of pods or project to an external system or service. Access via node IP means all pods running on a given node can access external systems.
OpenShift Container Platform 3.6 went GA on August 9, 2017. You can read more about the release and new features here. In this article we will setup a standard non-HA environment that is perfect for PoCs or labs. Before we begin, let’s explain OpenShift for those that may be starting their OpenShift journey today. OpenShift is a complete container application build + run-time platform built on Kubernetes (Container Orchestration) and Docker (Container Packaging Format). Organizations looking to adopt containerization for their applications need of course a lot more than just technology, (Kubernetes and Docker), they need a real platform. OpenShift provides a service catalog for containerized applications, huge selection of already certified application runtimes + xPaaS services, a method for building containerized applications (source to image), centralized application logging, metrics, autoscaling, application deployments (Blue-Green, A/B, Canary, Rolling), integrated Jenkins CI/CD pipelines, integrated docker registry, load balancing / routes to containerized apps, multi-tenant SDN, security features (SELinux, secrets, security context), management tooling supporting multiple OpenShift environments (CloudForms), persistent storage (built-in Container Native Storage), automated deployment tooling based on Ansible and much, much more. OpenShift is a platform that runs on any infrastructure, from bare-metal to virtualization to public cloud (Amazon, Google, Microsoft), providing portability across cloud infrastructure for containerized applications. All of these things together is what truly enables organizations to move to DevOps, increase application release cycles, speed up innovation cycles, scale efficiently, gain independence from infrastructure providers and deliver new capabilities faster with more reliability to their customers.
One of the most important capabilities of any platform in today’s service driven, pay-as-you-go economy is metering and showback. Without a solid understanding of costs, organizations are in fact unable to provide services. With containers, metering and showback becomes more challenging. If we think about containers simply being processes, then we are basically needing to meter and perform showback at that level of granularity. In addition since OpenShift uses Kubernetes for container orchestration, there are additional concepts that are new. For example, one more more containers run together in what Kubernetes refers to as a Pod. Next Pods are extremely dynamic and their lifetime very short. All of this make metering and showback anything but straight-forward. Thankfully OpenShift and CloudForms have the solution.