In December 2022, we introduced our partnership with Isovalent to deliver subsequent era prolonged Berkeley Packet Filter (eBPF) dataplane for cloud-native purposes in Microsoft Azure and it was revealed that the following era of Azure Container Community Interface (CNI) dataplane could be powered by eBPF and Cilium.
At present, we’re thrilled to announce the final 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 mission that makes use of eBPF-powered information airplane 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 implementing networking safety.
On this weblog, we are going to delve additional into the efficiency and scalability outcomes achieved by this highly effective networking providing in Azure Kubernetes Service.
Efficiency and scale outcomes
Efficiency assessments are performed in AKS clusters in overlay mode to investigate system habits and consider efficiency beneath heavy load circumstances. These assessments simulate situations the place the cluster is subjected to excessive ranges of useful resource utilization, akin to massive concurrent requests or excessive workloads. The target is to measure numerous efficiency metrics like response instances, throughput, scalability, and useful resource utilization to know the cluster’s habits and establish 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 device, 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 general completion time. It has been noticed that the service routing latency of Azure CNI powered by Cilium and kube-proxy initially exhibit related efficiency till the variety of pods reaches 5000. Past this threshold, the latency for the service routing for kube-proxy based mostly cluster begins to extend, whereas it maintains a constant latency stage for Cilium based mostly clusters.
Notably, when scaling as much as 16,000 pods, the Azure CNI powered by Cilium cluster demonstrates a big enchancment with a 30 % discount in service routing latency in comparison with the kube-proxy cluster. These outcomes reconfirm that eBPF based mostly service routing performs higher at scale in comparison with IPTables based mostly service routing utilized by kube-proxy.
Service routing latency in seconds
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 judge the efficiency of the cluster beneath excessive scale circumstances. 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 elements of the cluster’s efficiency beneath various circumstances.
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 companies deployed.
Throughout 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.
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 companies.
The CPU utilization of the Cilium agent remained beneath 150 milli cores and reminiscence is round 1 GiB. This demonstrated that Cilium maintained low overhead regardless that the variety of community insurance policies acquired doubled.
Scale check with 1k companies 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 companies, 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 take care of low overhead even when massive variety of companies acquired deployed and as now we have seen beforehand service routing through eBPF gives vital latency positive aspects for purposes and it’s good to see that’s achieved with very low overhead at infra layer.
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 significantly better with nodes, pods, companies, and community insurance policies whereas conserving overhead low. This product providing is now typically out there 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 atmosphere.
To get began as we speak, go to the documentation out there on Azure CNI powered by Cilium.
