Source URL: https://cloud.google.com/blog/products/containers-kubernetes/improving-gke-container-image-streaming-for-faster-app-startup/
Source: Cloud Blog
Title: Start and scale your apps faster with improved container image streaming in GKE
Feedly Summary: In today’s fast-paced cloud-native world, the speed at which your applications can start and scale is paramount. Faster pod startup times mean quicker responses to user demand, more efficient resource utilization, and a more agile development and deployment lifecycle overall. We’re continuously working to enhance the performance of Google Kubernetes Engine (GKE) to help you achieve these goals.Previously we introduced container image streaming in GKE, a feature designed to significantly reduce image pull times and accelerate application startup. Today, we’re excited to announce a new set of performance improvements to GKE container image streaming.These enhancements can help your GKE workloads start up faster and run more efficiently, particularly ones suffering from long startup times due to large container images. Specifically, AI/ML model serving applications will benefit from the improved startup times.What’s new?The performance boosts stem from a combination of targeted client-side innovations and ongoing optimizations to our image-streaming backend infrastructure.A key improvement on the client-side is new intelligent read-ahead capabilities. These allow GKE to proactively fetch image data that is likely to be requested next, minimizing the time your applications spend waiting for data during startup. This works in concert with improvements to the image streaming backend, ensuring that your containers get the data they need, when they need it — just faster.Alongside these client-side enhancements, we’ve made a number of improvements to our backend that help ensure that the image data is served efficiently and reliably, contributing to the overall speed and stability of the image streaming process.
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Measuring the gains: Our benchmarking approachWe benchmarked the performance of image streaming to quantify its benefits, using an internal benchmarking suite to compare historical image streaming data versus the latest version. The benchmark is based on the popular Triton Inference Server image, which we use to measure image loading performance.
Figure 1 Startup latency in ms: Blue GA image streaming, Green: Performance improved version
In general, we can see a up to ~30% improvement on the image streaming performance with the newly added enhancements. For large container images such as AI/ML models (e.g., vLLM-based containers, which often start at 8 GB and that can easily be as big as 100GB when you include model weights), this enhancement makes a big difference. By improving image pulling time, you get overall quicker container startup compared to non-streamed containers. These performance enhancements also play a big role in scaling out containers using horizontal pod autoscaler (HPA).Get started with faster image streaming todayThese image streaming performance improvements are automatically available to you when using GKE versions 1.32.1-gke.1729000 or newer. If you are already using container image streaming, there are no configuration changes you need to make to benefit from these enhancements. If you are not yet using image streaming, simply enable it on any new or existing GKE cluster to instantly get these benefits.Improved image streaming performance marks another milestone on our journey to provide you with the fastest and most efficient container management platform. We will roll out further improvements to image streaming focused on image availability, usability, reliability, and integration with other GKE capabilities.We encourage you to leverage these new enhancements to accelerate your application deployments on GKE. Check out the image streaming documentation to get started today and tap into a world of applications that start and scale faster!
AI Summary and Description: Yes
Summary: The text discusses recent performance enhancements to Google Kubernetes Engine (GKE) container image streaming capabilities that significantly improve application startup times, particularly beneficial for AI/ML model serving applications. The improvements stem from intelligent read-ahead capabilities and optimizations to the image-streaming backend infrastructure.
Detailed Description: The content revolves around advancements in GKE’s container image streaming which aim to enhance the speed and efficiency of application deployment. Below are the primary points of significance:
– **Importance of Speed in Cloud-Native Environments**: In a cloud-native context, faster application startup and scaling are essential for responding promptly to user demand and optimizing resource utilization.
– **New Performance Enhancements**: The announcement highlights a new set of performance improvements that target the startup times of workloads, particularly those with large container images, such as AI/ML model-serving applications.
– **Client-Side Innovations**:
– **Intelligent Read-Ahead Capabilities**: These allow GKE to preemptively fetch image data likely to be needed next, reducing waiting time during application startup.
– **Backend Optimizations**: Efforts to enhance the infrastructure that supports image streaming aim to serve image data more efficiently and reliably, contributing to improved overall performance.
– **Benchmarking Results**: The performance gains have been quantified using benchmarks that show an approximate 30% improvement in image streaming, especially beneficial for large container images (e.g., AI/ML models), which can start at 8 GB and extend up to 100 GB.
– **Impact on Scaling**: These enhancements also facilitate better performance in scaling out containers through the horizontal pod autoscaler (HPA).
– **No Configuration Required**: Current users of GKE can leverage the improvements without needing to adjust configurations, which simplifies transition and optimization.
– **Encouragement for Adoption**: The text encourages users to utilize the faster image streaming capabilities by upgrading to specific GKE versions, facilitating better and faster application deployments.
This information is critical for security and compliance professionals, as improved startup times can help in deploying security updates and patches more rapidly. It emphasizes the need for ongoing monitoring and assessment of cloud services, ensuring that enhancements do not introduce vulnerabilities amidst acceleration in deployment speeds.