KUBERNETES, MICROSERVICES, RESOURCE ALLOCATION
Best Practices for Improving Resource Allocation in Kubernetes
October 4, 2023 | 14 min read
Let’s assume your family is organizing a large dinner party. Due to health concerns, each family member has different dietary requirements and preferences, so you'll need to carefully spread ingredients and resources to ensure everyone has a filling meal. But then problems started to arise - some family members unexpectedly brought guests while others had larger appetites which led to a sudden rise in the demand for more food. So it became challenging to distribute food proportionately to everyone.
This is similar to the challenges of improving resource allocation in Kubernetes, where applications have varying resource requirements. It is critical to balance performance and cost while ensuring efficient resource use. When an application running in a Kubernetes cluster utilizes more resources (such as CPU, memory, or storage) than it should, it can cause performance concerns and system crashes. Worse, troubleshooting resource allocation issues in Kubernetes can be difficult, especially when working with a remote cluster.
In this article, we will look at common Kubernetes resource allocation issues, how to identify them, the problems they cause, and best practices on how to effectively optimize resource allocation in Kubernetes to achieve better performance and scalability.
Challenges of optimizing resource allocation in Kubernetes
Resource allocation is vital for ensuring the best possible performance and scalability of Kubernetes applications. Optimizing resource allocation in Kubernetes, however, is not without some challenges. Here are some of them:
- Kubernetes is a complex system with many moving parts. This can make it challenging to monitor resource usage and spot potential issues.
- Maintaining the best utilization of resources is difficult because applications may have different resource needs at different times.
- It can be challenging to optimize resource allocation holistically because there are times when allocating resources for one component can affect the performance and resource usage of other components.
- Kubernetes does not offer a lot of information on resource usage. This makes it challenging to recognize and fix resource allocation issues.
- Errors can easily result from the complex process of manually allocating resources in Kubernetes.
To overcome these challenges, developers would need to adopt proactive strategies. This includes implementing best practices for resource allocation, leveraging automation and orchestration tools, continuously monitoring resource usage, and employing scaling mechanisms. Improving resource allocation in Kubernetes is important to ensure efficient usage of your cluster's resources and to optimize the performance of your applications. In the next part of this article, we will focus on the best practices for overcoming the challenges we've just discussed, go through a few examples, and look at different tools that can be used to implement these best practices effectively.
Best practices for improving resource allocation in Kubernetes
Optimizing resource allocation in Kubernetes is an important aspect of maintaining application performance and controlling costs. Below are some best practices for improving resource allocation in Kubernetes:
1. Right-size resource limits: To optimize resource allocation for your Kubernetes pods, it's essential to determine the optimal resource limits based on your application's actual requirements. This can be done with resource requests and limits. Requests define the minimum amount of resources a container needs, while limits define the maximum amount it can consume. The aim is to avoid over-provisioning resources and preventing wastage and increased costs. This way you can also ensure predictable performance and efficient cluster utilization.
Imagine you have an eCommerce application with a microservices architecture. Each service will require different resource requirements. For example, the payment service might need more CPU than the inventory service. You could set the requests for this payment service to 100m CPU and 100Mi memory, and the limits to 200m CPU and 200Mi memory. This would ensure that your payment service always has at least 100m CPU and 100Mi memory available, but they would not be able to use more than 200m CPU and 200Mi memory.
2. Horizontal and Vertical AutoScaling: In autoscaling the decision to increase or decrease load is made automatically by system. Horizontal Pod Autoscaling allows you to automatically adjust the number of pods in deployments based on resource utilization or custom metrics. It allows the cluster to increase the number of pods as the demand for service response increases and decreases the number of pods as the requirement decreases. Vertical, on the other hand, automatically adjusts the number of replicas of a pod based on its resource usage.
For example, during a flash sale on your eCommerce platform, traffic spikes can be unpredictable. You can set up HPA based on CPU utilization or request latency metrics, your application can automatically scale up to meet demand and scale down during quieter periods, saving costs. You could also use VPA to automatically scale up the number of replicas on your web servers, and then scale them back down when traffic decreases.
Above, the minReplicas and maxReplicas fields specify the minimum and maximum number of replicas that the HPA can scale the Deployment to. The targetCPUUtilizationPercentage field specifies the percentage of CPU utilization that the HPA will use to determine when to scale the Deployment.
Here, the VPA will adjust the resource requests and limits of the ecomm-container container in the ecomm-deployment deployment to ensure that the container has at least 100m CPU and 100Mi memory available, but no more than 200m CPU and 200Mi memory.
3. Resource Quota: This is an object in Kubernetes that makes it easy to restrict cluster tenants’ resource usage per namespace. Resource quotas can be used to limit the amount of resources that a user or group of users can consume in a Kubernetes cluster. It prevents resource hogging in a Kubernetes cluster by limiting the amount of CPU, memory, and other resources that can be consumed by a namespace. This is especially important for multi-tenant clusters, as it can help prevent resource exhaustion and ensure that all users have fair access to resources. Imagine you decide to add a new ecommerce product to your offering for food delivery, and you add this new service to your namespace. Let's call this "Food Inc." If Food Inc is growing rapidly and deploying resource-intensive microservices, such as real-time inventory updates, image processing, and personalized recommendation engines. Without Resource Quotas in place, Food Inc. could potentially use up more cluster resources than the other tenants, leading to a degradation in the performance of these other clusters.
4. Monitoring and Resource Optimization Tools: Monitoring and Resource Optimization Tools in Kubernetes play a very important role in making sure your application functions as it should. Thanks to these, you can gain more insights into how resources are being consumed, how the application is performing overall, and if there are any potential bottlenecks or issues. After gathering all the data you need, you can then decide on how to optimize your resources effectively. Monitoring tools like Prometheus collect data on different aspects of your cluster, e.g., network traffic, CPU and memory usage, and other specific metrics. The data you get from this analysis can let you know if the application is consuming resources efficiently.
Monitoring tools also come with alerting tools most of the time to let you know when predefined thresholds have been exceeded before the user experience of your applications is affected. They also help you control infrastructure costs by identifying underutilized resources, optimizing resource requests and limits, and preventing over-provisioning.
For example, if your ecommerce platform is having a Black Friday sale and there is an increase in sales, monitoring tools can immediately alert you about a spike in CPU usage on the order processing service, assuming one occurred. This would allow you to investigate and optimize the resource allocation for that service in real-time to prevent downtime or any bad user experiences.
5. Profiling: Profiling is also another way to improve the performance of your Kubernetes cluster. It can help you identify and resolve performance bottlenecks as well as inefficiencies in your application. It helps you understand how your application uses resources and can lead to targeted optimizations. Thanks to profiling, you can understand how your application consumes resources such as CPU, memory, and disk I/O. This would help you understand which segments are more resource-intensive. You can also easily detect things such as memory leaks or excessive consumption, making it easy for you to optimize your data structures and free up more parts of your code.
With profiling, you can also easily identify what parts of your code are hotspots. These are simply sections executed frequently and consume more resources than the other. Lastly, profiling can help with performance benchmarking, making it easy to benchmark different versions of your application or even your optimization strategies to see which have the most impact on your resource efficiency.
For example, if you notice that your e-commerce platform experiences slow response times during peak shopping hours, using profiling tools like Perf, you can locate which database query within the product catalog service is causing high CPU usage. After you have gathered the needed data, you can then analyze the query's execution plan and optimize it to reduce its resource consumption.
6. Node Affinity and Anti-affinity: It is possible to constrain a Pod so that it is restricted to run on particular node(s). Node affinity and anti-affinity are Kubernetes features that allow you to do this, by controlling where pods are scheduled on nodes.
Node Affinity allows you to specify rules that influence the scheduling of pods to nodes based on node labels. For example, you could specify that a pod should be scheduled on nodes that have the label env=production. This makes sure that pods are placed on nodes that have certain characteristics, equally making sure that resources are allocated optimally.
Node Anti-affinity, on the other hand, allows you to specify rules that restrict pods from being scheduled onto nodes with specific characteristics or labels. For example, you could state that a pod should not be scheduled on nodes that have the label tier=backend. This makes sure that pods are not placed on nodes that already have some workloads running, helping to distribute workloads, ensuring scalability and improve reliability.
For example , you might have a service e.g. your product service, which might require access to high-speed SSD storage for database operations. You can use Node Affinity to schedule this service on nodes labeled as "high-ssd-storage."
You could also have a set of microservices that handle customer payments. To avoid scheduling all these payment processing pods on the same node to ensure high availability, you can use Node Anti-affinity to prevent pods from scheduling on nodes with existing payment processing workloads.
In conclusion, optimizing resource allocation in Kubernetes is Just like making sure everyone at your dinner party gets their fair share of food despite varying appetites and dietary requirements.
Kubernetes' resource allocation process is intricate and has the potential to affect system performance significantly. Kubernetes resource allocation must be optimized to maintain system performance and prevent crashes.
By following the best practices outlined, using monitoring and optimization tools, setting resource limits, sing autoscaling (horizontal and vertical), implementing resource quotas, and using node affinity and anti-affinity rules, you can strike the right balance between performance and cost, ensure resources are being used efficiently, scalability, and reliability in your Kubernetes cluster.