How to Find an Optimal Set of Nodes for A Kubernetes Cluster

Finding an optimal set of nodes for the Kubernetes cluster serves multiple purposes. It is quite an impactful solution implemented for successfully reducing the total costs of Kubernetes infrastructure. Optimization is highly significant task in cloud computing.

If you have optimally sized clusters for the workloads they perform, the tasks will be done efficiently and cost-effectively. There is no obvious way of arriving at an optimally-sized cluster, not even if you have an autoscaling setting. But it is an undeniable fact that: size matters the most for Kubernetes clusters!

The nodes number available in the cluster is an important aspect that helps identify the overall performance and availability related to the workloads. Well, if you think a “bigger number of nodes is always better,” you are mistaken.

Considering the Kubernetes cluster sizing strategy, you must always have the objective to maximize the performance delivering satisfactory results, not the node count. Capitalizing on namespaces is barely an intelligent strategy.

Why Kubernetes Cluster Size Matters?

The number of nodes in terms of the size of Kubernetes infrastructure is responsible for affecting performance and availability in the most perilous ways.

Talking about cluster performance, the greater the count of nodes means enhanced performance. It is not the node count that improves performance, but more significant nodes let you have more resources available for the cluster to use and complete tasks.

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Coming to the availability, node count is directly related. Having a greater count of nodes leaves you with a smaller chance of node failure. It saves you from disruption in the cluster’s availability.

Some Extremely Basic Rules of Thumb for Cluster Sizing

Are you reading this blog to learn about enlarging your cluster, you must remember: It doesn’t work on a one-size-fits-all strategy.

  • Having a minimum of one node for each container seems suitable for a production namespace or cluster.
  • You need a physical machine, for every pod used in a production namespace or cluster. Regardless of its functionality as its own or hosts virtual machines. It helps boost cluster availability.
  • In the case of having namespaces in a single cluster excelling six, you need to consider breaking the cluster into smaller optimal set.

Physical and Virtual Machine Nodes

Having a balanced mix of physical and virtual machines in your optimal set Kubernetes cluster definitely influences the overall performance and availability.

For those who don’t know, both physical servers and virtual machines act in Kubernetes as nodes. Both are equally high performers. In comparison, if your cluster has more virtual machine nodes, it will unlikely be as much reliable as the cluster with more physical servers. With a greater count of physical servers, the impact of the failure will be reduced.

It is recommended to run a minimum of a few virtual machines on every physical server while having virtual machines connected with the cluster serving as nodes. In case of nodes failure, the underlying physical server of the resources will only be lost.

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No healthy middle ground has been found for setting the ratio of physical machines and virtual machines yet!

Why More Nodes are Not Always Better

Many users are confused with the concept that more nodes added to your cluster can make your cluster work better. In reality, that’s not the case. The main reason is “not all nodes are created equal.”

There is an extraordinary amount of difference in what creates a node. You might find some nodes becoming a significant part of hardware resources lying within the cluster. These nodes will be responsible for doing so more in improving the cluster’s performance.

It means if you have a lesser number of nodes but all into hardware, the overall node count will be considered a fragile choice for presenting your cluster’s performance. In specific scenarios, few optimal set nodes are likely to become available in comparison to others.

A physical server residing in your local data center with no power backup is a less dependable node option than the virtual machines hosted within the cloud.

More Nodes Means More Complexity

Besides, having more nodes lead to more complicated operations. The more nodes your cluster has, the tougher it is to manage, provision, evaluate, and secure each node. Although Kubernetes is automated, a large node count will not be a real problem. But, still…it’s a factor to think about.

Always remember that performance and availability are interrelated. One cannot be maximized without focusing on the other, regardless of how many nodes you tend to have or don’t have. Instead of obsessing about the node count, you must aim to accomplish certain levels of performance and availability that seem acceptable and suitable to your requirements.

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Rightsizing your Kubernetes Cluster

Now the question is, “how can you ensure to have enough count of nodes with a balanced mix of physical and virtual machines?” You cannot and will never be able to find a universal answer! Try considering various factors!

  • Look how reliable do you have a physical infrastructure. Because having an ultra-reliable infrastructure is beneficial and will work brilliantly even if you have fewer nodes. Typically, a cloud-based Kubernetes deployment can work on fewer nodes than the on-premises one.
  • Now, consider the hardware profiles and check how many resources are needed for each node. Considering the performance, not many nodes are needed if every node is offering a comparatively high quantity of hardware resources.
  • The count of Master Nodes matter and matter more in comparison to worker nodes. For instance, a failure of a single master node can be disastrous, especially if it is your ONLY node from the master. More master nodes minimize your need for worker nodes.
  • Identifying the total number of workloads is essential as it determines how large your cluster should be. Dividing clusters into isolated zones is easier with Kubernetes optimal set namespaces for individual workloads. But, there is a point where you find breaking your large cluster into smaller, more efficient than adding more namespaces.

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