Before migrating, we operated using the old way, static servers. We had no flexibility, and we continuously needed to scale up to keep pace with the demands of our customers, a task that required manual intervention. The prospect of automatic scaling led us to the embrace of Kubernetes.
Kubernetes was our knight in shining armor, possessing the capability to scale up automatically and efficiently, depending on our needs. This newfound power was a stark contrast to the constant demand assessment and decisions we had to make previously.
As we were about to find out, every new adventure, no matter how promising, carries its set of challenges. We’ll be covering these three first-timer challenges with using K8s as your new center of operations.
High costs due to unoptimized deployments.
Steep learning curve of Kubernetes.
Optimizing HPA autoscaling for stability.
Firstly, while Kubernetes excelled at scaling up, the ease with which it scaled down was not as apparent. As Kubernetes begins to scale down, it does so by first taking down the pods, whereas the nodes usually stay running for a more extended period. This creates a situation where resources are wasted on servers that are no longer in use. The result? A spike in our expenses.
The second challenge is in the form of understanding the language of Kubernetes. If you are migrating from traditional Linux operations, you step into a different ecosystem with Kubernetes. All components, although similar, carry nuances that can dramatically impact their functioning. Among those, understanding the concepts of liveliness probes, resource requests and limits became a demanding task for us.
Optimizing autoscaling in Kubernetes was another riddle to solve. Kubernetes provides two levers, horizontal pod autoscaling (HPA) and vertical pod autoscaling (VPA). The former worked like a charm for our needs, but understanding how to manage resource allocation in terms of requests and limits proved more challenging. Learning the HPA strategies was crucial, as incorrect use could lead to low CPU utilization and results in an increased spend.
Requests and Limits in Kubernetes carry specific meanings that must be understood to implement them effectively. The "request" determines how a pod should be scheduled across nodes. Setting “CPU requests” too high and you will end up hurting your deployment. If your application is unable to use those resources, your workload distribution won’t leverage resources. As a result, servers with unused CPU power were running - yet another drain on resources and finance.
Deployment Limits in Kubernetes leverages cgroups to enforce a maximum cap on the resource utilization possible per pod. This prevents one application from consuming too many resources and causing problems for other applications. However, for most applications, it is important to set the HorizontalPodAutoscaler (HPA) to prevent over-utilization of resources and ensure that there is always enough capacity.
Mastering Kubernetes was indeed an uphill battle, requiring determination and concentration. As with any new adventure, the ups and downs contributed to strength, resilience, and an increased understanding of our newfound system’s operational capabilities. Kubernetes is fantastic.
Despite the challenges, our progress was steady. Our understanding of Kubernetes and its language improved every day. We have established a stable operation that continues to serve us well. However, we still aspire to optimize our systems and resources more effectively.
Reflecting on our progress, the fundamental challenges we encountered with Kubernetes revolved around cost and efficient resource utilization. While scaling up comes easy with Kubernetes, scaling down to control costs and avoid resource wastage requires a deeper understanding and fine tuning.
Scaling up and down when demand fluctuates offers immense potential when harnessed correctly. We are committed to further exploring this promising field and improving our understanding and execution of Kubernetes concepts.
Despite the trials faced, our team stands ready to overcome new challenges. The journey has been arduous at times, and the rewards will continue to be fruitful. The quest for optimal Kubernetes operation is long and winding. We’re confident that it will lead to a promising tomorrow.
Here we discus common questions that come up in first-time Kubernetes deployments. Frequently asked questions that you may have regarding costs and ways to control them.
Kubernetes is a platform that automates Linux container operations. It eliminates many of the manual processes involved in deploying and scaling containerized applications, making it much easier to manage and deploy applications at scale.
Kubernetes autoscaling works through its horizontal pod autoscaler (HPA) and vertical pod autoscaler (VPA). The HPA automatically scales the number of pods in a replication controller, deployment, or replica set based on observed CPU utilization, and the VPA adjusts the amount of allocated resources to current demand.
Kubernetes can scale down but not as efficiently as it scales up. It removes pods first and leaves nodes running longer, leading to potential underutilized resources and greater costs.
There can be challenges around setting the correct request limits, determining where to schedule a pod, and preventing applications from consuming more resources than necessary. Misconfiguration can lead to underutilization and higher costs.
In Kubernetes, 'requests' are what the system will guarantee for the container, and 'limits' dictate the maximum resources that a container can consume before the system will stop and restart it.
Inefficient scaling down and improper setting of requests and limits can lead to higher costs because of unused resources.
The challenges include learning a new set of commands and terminologies, understanding the new system structure, and reconfiguring applications to suit the Kubernetes environment.
Yes, Kubernetes carries a steep learning curve, especially with regard to understanding its unique nomenclature and resource management strategies.
This can be achieved through proper understanding and setting of ‘requests’ and ‘limits,’ as well as correct use of HPA and VPA for auto-scaling.
Horizontal Pod Autoscaling (HPA) adjusts the number of pod replicas in a replication controller or deployment based on CPU usage or custom metrics, while Vertical Pod Autoscaling (VPA) automatically adjusts the CPU and memory reservations for your pods to help ensure that resource usage matches demand.
Cost efficiency can be managed by ensuring efficient scaling down, optimizing resource utilization, and careful setting of ‘requests’ and ‘limits.’
Setting a high resource request limit can result in underutilized resources if the actual demand is low, leading to wastage and higher costs.
The 'requests' values are used for scheduling your containers to ensure there are enough resources for your application's needs. The 'limits' values, on the other hand, can help ensure that a single application does not consume all of a system's available resources.
Some recommended best practices include mastering the Kubernetes nomenclature, setting appropriate 'requests' and 'limits', understanding auto-scaling mechanisms, and having a strategy to manage costs.
Yes, Kubernetes can become expensive if not managed correctly, mainly due to inefficient scale-down and resource allocation leading to wastage. This can be mitigated by learning how Kubernetes works, mastering autoscaling, and setting up 'requests' and 'limits' adequately, among other things.
Kubernetes intelligently manages server hardware resources by deploying applications on servers based on their resource requirements ('requests'), and ensuring any one application does not over-consume resources ('limits'). From an auto scaling perspective, you’ll typically leverage cloud vendor plugins that provide K8s with the ability to provision and terminate server nodes as it needs.
In Kubernetes, CPU utilization should ideally correspond to the 'requests' and 'limits' set. However, improper settings can lead to low utilization of CPU, resulting in wastage of resources.
Transitioning from standard Linux to Kubernetes involves understanding containerized application management, learning new terminologies and commands, and shifting from manual scaling to automated scaling strategies.