3/20/2024 0 Comments Load balancer png![]() ![]() resource-group $CLUSTER_RESOURCE_GROUP \ SCALE_SET_NAME = $(az vmss list -resource-group $CLUSTER_RESOURCE_GROUP -query ".name" -o tsv ) # Add your local public key to the VMSS to enable ssh access # Get the managed cluster resource group and scale set namesĬLUSTER_RESOURCE_GROUP = $(az aks show -resource-group $RG -name kubenet-cluster -query nodeResourceGroup -o tsv ) To see this lets first setup SSH on our cluster and use ssh-jump to access a node. The client side ‘TCP Keep Alive’ setting will determine if an how long that outbound port will remain active. A common session will will obviously have the same source and destination IP and protocol, but additionally the outbound port from the source will be consistent. Hashing of the above provides some stickiness, specifically if all of the above match, which will happen if the requests are from a common session. In short the ALB creates a hash of the the following: ![]() If you take a look at the Azure docs for the ALB we can see that the default algorithm used by the ALB is hash based. Some of the most common and basic are ‘Round Robin’, ‘Statistic’ and ‘Hash Based’. Azure Load Balancer (ALB) to NodeĪny load balancer will have an algorithm it uses to determine where to send traffic. Let’s send some traffic to that endpoint and see how it behaves. Since we create our service as ‘type: LoadBalancer’ our cluster called out to Azure and created an Azure Load Balancer for us. Great! Now we have our network, our cluster and a deployment we can start to play with. NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES Before we create the cluster, however, we’ll need a network. We’ll deploy a set of simple web server pods and an nginx ingress controller. I’m going to start with an AKS cluster using the ‘Kubenet’ network plugin. Setupįor the analysis we’re going to run we’ll need a test cluster with some resources deployed. Let’s start by setting up our test cluster, and checking out the Azure Load Balancer. Before we get into advanced load balancing, I strongly recommend you read through those posts to help with some of the base concepts that will come into play here. As part of that, we also ran through the traffic flows at the kernel level via iptables. In previous posts I’ve run you through the full stack for the AKS network plugins Kubenet and Azure CNI. In particular, I want to help clarify the routing decisions that are made at each step of the flow and how that can impact your application behavior and performance. that I am probably drawing twice as fast from the center two belts than I am from the others, and if that is so.In the next few posts (yeah…I think this will require a few).we’re going to run through what the end to end traffic flow looks like for a packet going through an Azure Load Balancer, into a Kubernetes service, then an ingress controller and finally to a backend set of pods. Did I design it right to ensure that it will draw equally from the 6 belts? My instints say no. (1) The trivial balancer/compressor below takes from the 6 unloaded belts and outputs to one or two belts. So, we need an "input-balanced" balancer.Īt this point I have 2 questions, one simple and one not so simple: And yet, to ensure smooth unloading, we want to pull exactly the same number of items from each of the belts coming from the train. Most of the balancer discussion and designs available are about balancing the *output* of a set of multiple belts, and some about balancing the right and left lane of a belt.īut there is very little know-how available on balancing the consumption of items across multiple belts on the *input* side, no matter what happens on the output side and that is what this thread is about.įor example, imagine a situation where you have 6 belts coming out of unloading a wagon, but your consumption (for whatever reason) only pulls items from the 1st belt. ![]()
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