A multi-cloud strategy is hard. Fortunately, Kubernetes makes it easier. In several respects, Kubernetes helps to solve or work around the most pressing pain points within a multi-cloud architecture.
Let’s explore how.
Multi-cloud architectures can deliver a number of benefits, including better cost-efficiency, reliability and scalability. Yet those benefits come with tradeoffs.
The reason why is simple: The more clouds you have, the more challenging it is to configure and manage them. Multi-cloud models involve more integrations between different clouds and different services, more accounts to oversee, more vendor-specific tools and processes to worry about, and so on.
And if your multi-cloud strategy includes a hybrid cloud (which it does if you have on-premises infrastructures or private clouds running alongside public clouds), integrating and managing all of the complexity becomes even more challenging.
Using Kubernetes to address multi-cloud challenges
Kubernetes can’t magically solve every multi-cloud challenge. But it can make a number of multi-cloud pain points easier to manage. In particular, consider the following ways in which Kubernetes can help reduce the complexity and risk of a multi-cloud strategy.
1Provisioning multi-cloud with Kubernetes
One of the first challenges you will face as you attempt to roll out a multi-cloud infrastructure is provisioning all of your clouds.
When you have a single cloud, you can use vendor-supplied provisioning tools (like AWS CloudFormation) to set up your workloads in an efficient and automated way. But those tools typically don’t work with third-party clouds.
You could also use provisioning tools that are not tied to any specific cloud vendor, such as Terraform or Ansible. That approach would help, but you will still likely find that you need to customize or tweak your configurations for the different clouds you are targeting.
With the help of Kubernetes, however, you can avoid all of this hassle. Kubernetes doesn’t care which cloud it is running on. Thus, if you host your workloads in Kubernetes, you can use the same configurations on any or all of the clouds that comprise your architecture. And because Kubernetes defines its configurations as code, you get the same level of automation and efficiency in provisioning as you would from a traditional provisioning tool.
2Multi-cloud monitoring challenges
For similar reasons to those described above, monitoring multiple clouds is difficult. You can’t rely on monitoring tools from individual vendors. And if you use third-party APM tools, you still typically have to tailor them to each of the clouds within your infrastructure.
When your workloads are standardized to run in Kubernetes, however, the only thing your monitoring tools need to be configured to monitor, is Kubernetes. (Well, maybe some basic monitoring of your cloud infrastructure would be wise, too, but Kubernetes becomes the central focus of your monitoring efforts.) Kubernetes supplies a rich set of metrics that can be used to track workload availability and health, no matter which cloud or clouds are hosting your Kubernetes clusters.
3Varying skillsets for multi-cloud administration
Another common multicloud challenge is the need for developers and IT teams to learn all of the different clouds within your architecture. Engineers who know the ins-and-outs of AWS may not be as skilled when it comes to Azure and GCP, for example. And if you have private cloud platforms like OpenStack in the mix, the skills required to administer your multi-cloud architecture become even more diverse.
A Kubernetes-based strategy significantly simplifies these requirements. It allows your engineers to focus primarily on Kubernetes. They may still need to have some basic ability to work with whichever clouds (or on-premises infrastructure) you are using to host Kubernetes, but their main focus shifts to Kubernetes.
Much has been written about the challenges of multi-cloud security. The more clouds you have, the larger your attack surface, and the more potential security vulnerabilities you are exposed to.
Here again, however, relying on Kubernetes to host your workloads helps to simplify security configurations and reduce your attack surface. Although Kubernetes doesn’t completely eliminate the need to secure underlying cloud infrastructures using each cloud’s IAM framework, or to monitor the infrastructure as a whole for security incidents, Kubernetes does help to standardize your configurations in order to reduce the risk of oversights that could create security vulnerabilities.
At the same time, Kubernetes-native features, like pod security policies, network policies and RBAC, provide an additional set of tools to help protect workloads. These would not be available in a multi-cloud architecture that did not incorporate Kubernetes.
5Redundancy and availability
One of the most common reasons to adopt multi-cloud is to increase workload availability by hosting redundant instances of the same workload.
You can certainly achieve redundancy without Kubernetes. But it’s harder to do, because your redundant workloads would in most cases not be identical. Instead, you might set up a VM on AWS, and another one on Azure. Even if each of these instances hosts the same workload, each one would need to be configured somewhat differently, according to the needs of the cloud that hosts it.
If you containerize your applications and host them on Kubernetes, however, redundancy is much easier to achieve. You can configure the workload once and then reproduce it across as many clouds as you like – because, again, Kubernetes doesn’t care which cloud it is hosted on.
6Controlling multi-cloud costs
Multi-cloud is also often touted as a way to reduce costs, because it lets you pick and choose the most cost-efficient solutions from among multiple cloud vendors.
That’s true. However, it’s also true that the more clouds you have, the easier it is to spin up unnecessary workloads, or otherwise waste cloud resources. If you do this, you undercut the cost-efficiency goals of your multi-cloud strategy.
While it’s certainly possible to use Kubernetes in a cost-inefficient way, it is arguably harder to make cost-related mistakes with Kubernetes. That is due, in part, to the fact that Kubernetes standardizes workloads and configurations across all clouds. That makes it easier to set up environments in a consistently cost-efficient way, without worrying about the nuances of each cloud provider. At the same time, pricing for managed Kubernetes services tends to be more clear-cut than do pricing schemes for many other types of cloud services. There are fewer variables, which makes costs easier to predict.
Kubernetes won’t solve every multi-cloud problem you’ll ever face. And it’s certainly possible to execute a successful multi-cloud strategy without Kubernetes. But if you take that approach, you’ll likely find your multi-cloud architecture to be more difficult to provision, monitor, secure and manage than you would if you rely on Kubernetes to abstract your workloads away from the various underlying clouds, thereby simplifying the inherent complexity of multi-cloud infrastructures.
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