Sunday, May 26, 2024

Azure CNI with Cilium: Most scalable and performant container networking within the Cloud | Azure Weblog


In December 2022, we introduced our partnership with Isovalent to convey subsequent era prolonged Berkeley Packet Filter (eBPF) dataplane for cloud-native functions in Microsoft Azure and it was revealed that the subsequent era of Azure Container Community Interface (CNI) dataplane can be powered by eBPF and Cilium.

Right this moment, we’re thrilled to announce the overall availability of Azure CNI powered by Cilium. Azure CNI powered by Cilium is a next-generation networking platform that mixes two highly effective applied sciences: Azure CNI for scalable and versatile Pod networking management, built-in with the Azure Digital Community stack, and Cilium, an open-source challenge that makes use of eBPF-powered knowledge aircraft for networking, safety, and observability in Kubernetes. Azure CNI powered by Cilium takes benefit of Cilium’s direct routing mode inside visitor digital machines and combines it with the Azure native routing contained in the Azure community, enabling improved community efficiency for workloads deployed in Azure Kubernetes Service (AKS) clusters, and with inbuilt assist for imposing networking safety.

On this weblog, we’ll delve additional into the efficiency and scalability outcomes achieved by means of this highly effective networking providing in Azure Kubernetes Service.

Efficiency and scale outcomes

Efficiency assessments are performed in AKS clusters in overlay mode to research system habits and consider efficiency underneath heavy load situations. These assessments simulate situations the place the cluster is subjected to excessive ranges of useful resource utilization, corresponding to giant concurrent requests or excessive workloads. The target is to measure numerous efficiency metrics like response occasions, throughput, scalability, and useful resource utilization to grasp the cluster’s habits and determine any efficiency bottlenecks.

Service routing latency

The experiment utilized the Customary D4 v3 SKU nodepool (16 GB mem, 4 vCPU) in an AKS cluster. The apachebench software, generally used for benchmarking and cargo testing net servers, was used for measuring service routing latency. A complete of fifty,000 requests have been generated and measured for total completion time. It has been noticed that the service routing latency of Azure CNI powered by Cilium and kube-proxy initially exhibit comparable efficiency till the variety of pods reaches 5000. Past this threshold, the latency for the service routing for kube-proxy primarily based cluster begins to extend, whereas it maintains a constant latency degree for Cilium primarily based clusters.

Notably, when scaling as much as 16,000 pods, the Azure CNI powered by Cilium cluster demonstrates a big enchancment with a 30 p.c discount in service routing latency in comparison with the kube-proxy cluster. These outcomes reconfirm that eBPF primarily based service routing performs higher at scale in comparison with IPTables primarily based service routing utilized by kube-proxy.

Service routing latency in seconds

Service routing latency in seconds with single service and different number of pods in backend.
Service routing latency in seconds with single service and totally different variety of pods in backend.

Scale check efficiency

The size check was performed in an Azure CNI powered by Cilium Azure Kubernetes Service cluster, using the Customary D4 v3 SKU nodepool (16 GB mem, 4 vCPU). The aim of the check was to guage the efficiency of the cluster underneath excessive scale situations. The check centered on capturing the central processing unit (CPU) and reminiscence utilization of the nodes, in addition to monitoring the load on the API server and Cilium.

The check encompassed three distinct situations, every designed to evaluate totally different features of the cluster’s efficiency underneath various situations.

Scale check with 100k pods with no community coverage

The size check was executed with a cluster comprising 1k nodes and a complete of 100k pods. The check was performed with none community insurance policies and Kubernetes providers deployed.

In the course of the scale check, because the variety of pods elevated from 20K to 100K, the CPU utilization of the Cilium agent remained constantly low, not exceeding 100 milli cores and reminiscence is round 500 MiB.

Average CPU utilization in Millicore by cilium agent pods for creating different number of pods without network policies and services.
Cilium common CPU utilization for creating 100k pods.
Average Memory utilization in Mebibytes by cilium agent pods for creating different number of pods without network policies and services.
Cilium common reminiscence utilization for creating 100k pods.

Scale check with 100k pods with 2k community insurance policies

The size check was executed with a cluster comprising 1K nodes and a complete of 100K pods. The check concerned the deployment of 2K community insurance policies however didn’t embrace any Kubernetes providers.

The CPU utilization of the Cilium agent remained underneath 150 milli cores and reminiscence is round 1 GiB. This demonstrated that Cilium maintained low overhead regardless that the variety of community insurance policies received doubled.

Average CPU utilization in Millicore by cilium agent pods for creating different number of pods with 2k network policies and without services.
Cilium common CPU utilization for creating 100k pods, 2k community insurance policies.
Average CPU utilization in Millicore by cilium agent pods for creating different number of pods with 2k network policies and without services.
Cilium common reminiscence utilization for creating 100k pods, 2k community insurance policies.

Scale check with 1k providers with 60k pods backend and 2k community insurance policies

This check is executed with 1K nodes and 60K pods, accompanied by 2K community insurance policies and 1K providers, every having 60 pods related to it.

The CPU utilization of the Cilium agent remained at round 200 milli cores and reminiscence stays at round 1 GiB. This demonstrates that Cilium continues to keep up low overhead even when giant variety of providers received deployed and as we have now seen beforehand service routing through eBPF gives vital latency beneficial properties for functions and it’s good to see that’s achieved with very low overhead at infra layer.

Average CPU utilization in Millicore by cilium agent pods after for 1k services different number of backend pods and with 2k network policies.
Cilium common CPU utilization for creating 1k providers with 60k pod backends, 2k community insurance policies.
Average Memory utilization in Mebibytes by cilium agent pods for creating 1k services with different number of backend pods and with 2k network policies.
Cilium common reminiscence utilization for creating 1k providers with 60k pod backends, 2k community insurance policies.

Get began with Azure CNI powered by Cilium

To wrap up, as evident from above outcomes, Azure CNI with eBPF dataplane of Cilium is most performant and scales a lot better with nodes, pods, providers, and community insurance policies whereas protecting overhead low. This product providing is now usually accessible in Azure Kubernetes Service (AKS) and works with each Overlay and VNET mode for CNI. We’re excited to ask you to attempt Azure CNI powered by Cilium and expertise the advantages in your AKS setting.

To get began at this time, go to the documentation accessible on Azure CNI powered by Cilium.



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