Optimizing Kubernetes for performance: benchmarking and tuning

[ServicesExperts]

Here’s how to identify and eliminate bottlenecks and inefficiencies to get the most out of your Kubernetes environment.

By Alex Tesch, Senior Consultant, Cloud Native Computing Practice, HPE Advisory & Professional Services

Kubernetes has rapidly emerged as the de facto standard for containers orchestration in recent years. It enables developers to manage, deploy, and scale containerized applications across a cluster of nodes, making it easier to manage applications in a distributed environment. However, as with any system, Kubernetes needs to be optimized to ensure top performance. Performance benchmarking and tuning in Kubernetes play a crucial role in achieving this goal.

Performance benchmarking in Kubernetes involves the process of evaluating the performance of the Kubernetes cluster to identify any bottlenecks, inefficiencies, and areas that require improvement. This is done by testing the Kubernetes environment under different workloads and measuring key performance metrics such as response time, throughput, and resource utilization. Performance benchmarking helps to identify the maximum capacity of the Kubernetes cluster and the limits of its scalability.

The first step in performance benchmarking is to identify the key performance metrics that need to be measured. These metrics may include CPU and memory utilization, network latency, and storage performance. Once the metrics have been identified, the next step is to design and execute the benchmark tests. The tests should be designed to simulate real-world workloads and should be executed under different load levels to determine the maximum capacity of the Kubernetes cluster.

Once the benchmark tests have been executed, the results need to be analyzed to identify any bottlenecks or inefficiencies in the Kubernetes environment. The analysis should include identifying any resource constraints, network latency issues, or storage performance issues that may be impacting performance. Based on the results of the analysis, the necessary optimizations can be made to improve the performance of the Kubernetes cluster.

Tuning Kubernetes

Kubernetes tuning involves the process of optimizing the Kubernetes environment to improve its performance. Tuning can be done at different levels, including node tuning, pod tuning, and application tuning:

• Node tuning involves optimizing the configuration of the Kubernetes nodes to ensure they are running at peak performance. This may include adjusting the CPU and memory limits, enabling kernel optimizations, and tuning the network settings.
• Pod tuning involves optimizing the configuration of the Kubernetes pods to ensure they are running at peak performance. This may include adjusting the resource requests and limits, configuring the pod scheduler, and tuning the container runtime settings.
• Application tuning involves optimizing the configuration of the application running on Kubernetes to ensure it is running at peak performance. This may include optimizing the application code, configuring the application runtime settings, and tuning the database settings.

One of the most widely used tools to benchmark application performance in K8 is Vegeta, a command-line HTTP load testing tool that can be used to test the performance of web applications in real time.

They are a few steps to configure Vegeta and run it against an application in a Kubernetes. Below we list the summary of steps to undertake for a successful deployment:

1. Install Vegeta. You can download the latest release of Vegeta from its GitHub repository. Once you have downloaded the binary, make sure it's executable and place it in a directory that is included in your system's PATH

2. Create a Target List: Create a text file containing a list of URLs or endpoints that you want to test. Each line should contain a single URL. Save the file with a descriptive name; for example, target_list.txt.

3. Create a Request File. Create a text file containing the HTTP request that you want to send to the endpoints in your target list. You can include headers, query parameters, and request bodies in this file. Save the file with a descriptive name; for example, request.txt.

4. Run Vegeta. Open a terminal window and navigate to the directory where you have saved the target list and request files. Run the following command to execute the test: 

vegeta attack -targets=target_list.txt -duration=30s -rate=10 -body=request.txt | vegeta report

This command will send 10 requests per second to the endpoints in your target list for 30 seconds. You can adjust the rate and duration according to your needs. The attack command sends the requests, and the report command generates a report based on the results.

5. Analyze the results. The report command will output a summary of the test results, including the total number of requests, the success rate, and the average response time. You can also generate graphs and charts to visualize the results using the Vegeta plot command.

By using Vegeta to test performance in a Kubernetes cluster, it is possible to simulate a large number of users accessing the application and get accurate performance metrics related to the load. Vegeta provides the capabilities to plot a graph to show the performance of the application based on the parameters provided.

Performance benchmarking and tuning are essential to ensure optimal performance in Kubernetes. Performance benchmarking enables the identification of any bottlenecks and inefficiencies in the Kubernetes environment, while tuning enables the optimization of the Kubernetes environment to improve its performance.

HPE Advisory and Professional Services can assist customers in their journey to Kubernetes performance optimization through service offerings such as Cloud Native Computing Advisory Service – Kubernetes Performance Optimization, which follows best practices for benchmarking and K8 tuning. Organizations can achieve better performance and scalability in their Kubernetes environments, leading to improved application performance and better end-user experience. To learn more, see our HPE Container Adoption Solution Brief.

Learn more about advisory and professional services from HPE Services.

Alex Tesch has been working with open-source enterprise technologies for most of his 22-year IT career in companies like Red Hat, IBM, Sun Microsystems, and now Hewlett Packard Enterprise. Alex is currently an APJ lead consultant in HPE’s Hybrid IT Center of Excellence, where he designs and evangelizes cloud-native solutions that help customers to modernize their infrastructure and adopt new best practices to leverage next-generation IT.