Kubernetes Lifecycle Management in a Multi-Cloud Environment: Best Practices
May 17, 2023
When discussing lifecycle management in the context of an IT product, Day 0, Day 1, and Day 2 are frequently mentioned. The “Design” phase, which begins on Day 0, is when you decide what you’ll be deploying. The “Deploy” phase, or the first day the product is used in your environment, begins on Day 1. The “Operate” phase, or “Day 2+” as it should more properly be called, begins on Day 2. In terms of Kubernetes lifecycle management.
A multi-cluster Kubernetes deployment consists of two or more clusters. The Kubernetes clusters in a multi-cluster deployment don’t have to run in different clouds; they might all run in the same local data center if they are deployed outside of a cloud. However, because one of the main advantages of multi-cluster Kubernetes is frequently the ability to distribute workloads across a larger geographic area, it’s typical to see this form of deployment within a multi-cloud architecture. it is not necessary to operate a multi-cluster deployment using a single control plane or interface. Technically, you could create two different clusters, operate them with totally different tools, and claim to be a multi-cluster corporation. However, it’s a wasteful strategy. Multiple-cluster installations that are controlled by a single platform are far more typical.
A multi-cluster deployment typically has many masters, it is not necessarily required. Therefore, multi-master (or, for that matter, multi-tenant) deployments should not be confused with multi-cluster Kubernetes installations.
Benefits of Multicluster K8S Deployment
Performance, workload isolation, flexibility, and security are the four major advantages of a multi-cluster implementation.
Several options exist for a multi-cluster deployment to enhance Kubernetes performance:
Lower latency: Multi-cluster Kubernetes makes it simpler to deploy workloads close to various user groups by offering your team the option to install numerous clusters. You might, for instance, host one cluster on the cloud and another in a colocation facility that is nearer to one of your target markets. You can lower latency by shortening the physical distance between your users and your clusters.
Availability: The availability of your workload can be increased by using multiple clusters. In the case that another cluster fails, you might use one cluster as a failover or backup environment. Additionally, you reduce the chance that the failure of one data center or cloud will affect all of your workloads by distributing clusters across other data centers and/or clouds.
Scalability of workloads: Running many clusters may increase your capacity to increase workloads as needed. When everything runs in a single cluster, it can be more difficult to identify whether specific workloads require extra resources or replicas, especially if performance data is lacking for certain workloads (which can happen if you are simply monitoring cluster-level health). When everything is running in a single cluster, “noisy neighbor” problems are also more likely to arise. The maximum cluster size that Kubernetes supports, which is presently 5,000 pods per cluster, may also be reached for very large clusters.
The maximum amount of isolation across workloads is provided by running distinct workloads in different clusters. There is almost no potential for workloads in different clusters to communicate with one another or use each other’s resources.
However, Kubernetes workload isolation is not just possible with multi-cluster deployments. Additionally, you can create several namespaces within of a single cluster. At least in theory, namespaces rigidly isolate workloads from one another. Resource quotas and network policies can be used to create some kind of isolation between pods even at the pod level. However, none of these approaches provide the absolute isolation that a multi-cluster setup does. When several teams or departments want to run workloads on Kubernetes but don’t want to worry about privacy or noisy neighbors, workload separation is especially crucial. Additionally, it is frequently preferred if your team wants to keep a dev/test environment distinct from a production environment or if you are experimenting with different Kubernetes settings and don’t want to run the risk of making a configuration change that might affect workloads in production.
Kubernetes gives you fine-grained control over how each cluster is setup when you run several clusters. For each cluster, you may, for example, employ a separate CNI or a different version of Kubernetes.
If your application depends on a certain configuration or version of a stack tool, the configuration flexibility of multi-cluster deployments is advantageous. It’s useful if you want to test new versions of Kubernetes in a separate development or test cluster before updating your production clusters to the new version.
Security and Compliance
Additionally, multi-cluster Kubernetes’ workload isolation gives some advantages in terms of security and compliance. Strict separation across workloads reduces the possibility that a security problem in one pod may spread to affect other pods. Again, there are other ways to obtain this benefit in addition to multi-cluster deployment. Other capabilities in Kubernetes, such as pod security policies, can assist prevent security problems from getting worse. However, segmenting workloads into distinct clusters ultimately offers the best isolation.
Running numerous clusters may make it simpler to adhere to some compliance regulations, which is a second aspect to take into account from a compliance standpoint. You can deploy a cluster in a place that meets those objectives while running other clusters elsewhere, for instance, if regulatory restrictions force you to keep some workloads on-premises or keep data inside a specific geographic region.
The best Kubernetes cluster management tool enables you to manage application lifecycles across hybrid environments and gives you visibility into your clusters. The Coredge Kubernetes Platform manages clusters and apps from a single console and has built-in security controls. Enterprises encounter problems when operating in a range of environments, including various data centers and private, hybrid, and public clouds. The Coredge Kubernetes Platform gives enterprises the tools they need to overcome their problems.