VM_CPU1_Meme1.5

Okay, let's break down how Kubernetes resource requests and limits for your Pods map to the underlying Azure VM compute in an AKS (Azure Kubernetes Service) cluster, and how this drives costs.

Core Concepts:

  1. Pods: The smallest deployable units in Kubernetes. They contain one or more containers.

  2. Resource Requests: The amount of CPU and memory that Kubernetes guarantees to a Pod. The Kubernetes scheduler uses requests to decide which node to place a Pod on. A Pod will not be scheduled if its requests cannot be met by any available node.

  3. Resource Limits: The maximum amount of CPU and memory a Pod (or specifically, its containers) can consume.

    • CPU Limits: If a container tries to use more CPU than its limit, it will be throttled (its CPU usage will be artificially slowed down).

    • Memory Limits: If a container tries to use more memory than its limit, it might be terminated by the Kubelet (OOMKilled - Out Of Memory Killed).

  4. Nodes: Worker machines in your Kubernetes cluster. In AKS, these are Azure Virtual Machines (VMs).

  5. Node Pools: A group of nodes within an AKS cluster that share the same configuration (VM size, OS, etc.). You can have multiple node pools with different VM SKUs.

  6. Allocatable Resources: Not all resources of a VM are available for Pods. Some resources are reserved for the operating system, the Kubelet (Kubernetes agent on the node), and other system daemons (like containerd or dockerd). The resources remaining after these reservations are called "allocatable resources."

  7. VM SKUs: Azure offers various VM sizes (e.g., Standard_DS2_v2, Standard_F4s_v2) with different amounts of vCPUs, RAM, storage, and associated costs.

The Mapping: From Pods to VMs and Cost

Here's how it connects, using your example:

resources:
  requests:
    memory: "2.5Gi" # Pod is guaranteed 2.5 GiB of RAM
    cpu: "1"        # Pod is guaranteed 1 vCPU core
  limits:
    memory: "4Gi"   # Pod can use up to 4 GiB RAM before being OOMKilled
    cpu: "1.5"      # Pod can use up to 1.5 vCPU cores before being throttled

  1. Node Selection (Scheduling based on Requests):

    • When you deploy a Pod, the Kubernetes scheduler looks at its requests (1 CPU, 2.5Gi memory).

    • It then scans the nodes in your cluster to find one that has enough allocatable CPU and memory to satisfy these requests.

    • Key Point: The sum of the requests of all Pods running on a node cannot exceed that node's allocatable resources.

  2. VM Capacity and Allocatable Resources:

    • Let's say you have a node pool with Standard_DS2_v2 VMs.

      • Total Capacity: 2 vCPUs, 7 GiB RAM.

    • AKS reserves some of this for system overhead. The amount reserved can vary, but a common rule of thumb for kube-reserved is:

      • 25% of the first 4GB of RAM

      • 20% of the next 4GB of RAM (up to 8GB)

      • 10% of the next 8GB of RAM (up to 16GB)

      • 6% of the next 112GB of RAM (up to 128GB)

      • 2% of any RAM above 128GB

      • And similar reservations for CPU (e.g., 6% of 1st core, 1% of next core, etc. or fixed amounts).

    • For a Standard_DS2_v2 (2 vCPUs, 7 GiB RAM):

      • Approx. Memory Reserved: (25% of 4GiB) + (20% of 3GiB) = 1GiB + 0.6GiB = 1.6GiB (plus some for system-reserved and eviction thresholds). Let's estimate a total of ~1.5-2.0 GiB reserved.

      • Approx. CPU Reserved: Small fraction, e.g., 0.1-0.2 vCPU.

    • Allocatable on Standard_DS2_v2 (Example Estimation):

      • Allocatable CPU: ~1.8 vCPUs

      • Allocatable Memory: ~5.0 - 5.5 GiB

    • You can see the exact allocatable resources by running kubectl describe node <your-node-name>. Look for the Allocatable section.

  3. Fitting Pods onto Nodes:

    • With your Pod requesting 1 CPU and 2.5Gi memory:

    • On our estimated Standard_DS2_v2 (allocatable: ~1.8 vCPU, ~5.0 GiB memory):

      • CPU-wise: We can fit 1 Pod (1 / 1.8 is okay). We can't fit two as 2 * 1 CPU = 2 vCPU > 1.8 vCPU.

      • Memory-wise: We can fit 2 Pods (2 * 2.5GiB = 5GiB, which is <= ~5.0 GiB).

    • Since a Pod needs both CPU and memory, the more constrained resource dictates. In this simple case, if all Pods are identical, CPU is the constraint for the number of Pods (1 per node using CPU request). However, if another Pod type was memory-heavy, memory could become the constraint.

    • Therefore, for Pods with requests: cpu: "1", memory: "2.5Gi", you can schedule at most ONE such Pod on a Standard_DS2_v2 if we strictly follow the CPU request as the primary constraint for scheduling additional identical Pods. (Actually, one Pod would leave 0.8 CPU and 2.5Gi memory, not enough for another identical pod's CPU request).

  4. Driving VM Count (Instance Pools):

    • If you need to run, say, 10 of these Pods, and each Standard_DS2_v2 can effectively host 1 due to CPU request (or a slightly different number if memory becomes the constraint for a mix of pods), you would need approximately 10 Standard_DS2_v2 VMs in your node pool.

    • The Cluster Autoscaler (if enabled in AKS) monitors for Pods that cannot be scheduled due to insufficient resources. If it sees pending Pods, and your node pool is configured to scale, it will provision new VMs (nodes) up to the maximum limit you've set for that node pool.

  5. Cost Implications:

    • Azure charges you for the VMs running in your node pools, based on their SKU and uptime.

    • If your Pods have high resource requests, you'll need either:

      • More VMs of a smaller SKU.

      • Fewer VMs but of a larger (and more expensive per instance) SKU.

    • The cost is directly tied to: (Number of VMs in Node Pool 1 * Cost per hour of VM SKU 1) + (Number of VMs in Node Pool 2 * Cost per hour of VM SKU 2) + ...

    • Therefore, your Pod requests are the primary driver for the number and/or size of VMs you need, which directly translates to Azure compute costs.

The Role of Limits:

  • Limits do not directly influence scheduling or the number of VMs needed. Scheduling is based on requests.

  • Limits are about resource capping during runtime:

    • If limits.cpu is set higher than requests.cpu (like in your example: request 1, limit 1.5), your Pod can "burst" and use up to 1.5 CPU cores if they are available on the node (i.e., not being used by other Pods). This makes your Pod "Burstable" in Kubernetes QoS terms.

    • If limits.memory is set, and your Pod exceeds it, it gets OOMKilled. This is a hard cap.

  • Setting appropriate limits helps prevent "noisy neighbor" problems where one misbehaving Pod consumes all node resources, impacting other Pods.

  • If limits are very close to requests (or equal), the Pod is "Guaranteed" QoS. This is predictable but might be less efficient if your app rarely hits the limit.

Steps to Understand Your Cluster's Resource Mapping and Costs:

  1. Identify VM SKUs in your Node Pools:

    • Go to your AKS cluster in the Azure Portal -> Node pools. Note the "VM size" for each pool.

    • Or use kubectl get nodes -o wide to see the instance type (though this might be an internal Azure name, the portal is clearer for SKUs).

  2. Check Node Allocatable Resources:

    • For a representative node in each pool: kubectl describe node <node-name>

    • Look for the Capacity and Allocatable sections.

    Capacity:
  cpu:                2
  ephemeral-storage:  129299100Ki
  hugepages-1Gi:      0
  hugepages-2Mi:      0
  memory:             7117592Ki  # Approx 7 GiB
  pods:               110
Allocatable:
  cpu:                1900m      # 1.9 vCPU
  ephemeral-storage:  119152022Ki
  hugepages-1Gi:      0
  hugepages-2Mi:      0
  memory:             5000000Ki  # Approx 5 GiB <--- This is a simplified example, AKS has its own calc.
                                  # Real AKS node for 7GiB VM might show around 5.5-5.8GiB allocatable.
  pods:               110

    • Note on AKS Allocatable Memory: AKS calculates allocatable memory based on a formula that includes a base amount for the Kubelet and OS, plus a percentage for kube-reserved and system-reserved, and an eviction threshold. The formula is roughly: allocatable = total_memory - (kube-reserved) - (system-reserved) - (eviction-threshold). For a 7GiB VM, kube-reserved is MIN(2GiB, 0.25 * 7GiB) = 1.75GiB. Then subtract system-reserved (often 100MiB) and eviction threshold (e.g., 100MiB). So, 7GiB - 1.75GiB - 0.1GiB - 0.1GiB = ~5.05GiB. The actual calculation is more nuanced, and kubectl describe node is the source of truth.

  3. Examine Your Pods' Requests and Limits:

    • kubectl get pods -o wide to see which nodes your pods are on.

    • kubectl describe pod <pod-name> to see its requests and limits.

    • For deployments: kubectl describe deployment <deployment-name>.

  4. Analyze Bin Packing:

    • Sum the requests of all Pods running on a particular node. This sum should be less than or equal to the node's Allocatable resources.

    • This will help you understand how efficiently your nodes are being utilized. If Allocatable is much higher than the sum of requests, you might be over-provisioning (or have room for more Pods/bursting).

  5. Relate to Azure Costs:

    • In the Azure Portal, go to "Cost Management + Billing".

    • You can filter costs by resource group (your AKS cluster's node resource group, usually MC_<resource-group-name>_<aks-cluster-name>_<region>).

    • This will show you the cost incurred by the VMs in your node pools.

    • By understanding how many Pods fit on a node, and how many Pods you need to run, you can directly see how Pod resource settings translate to the number/size of VMs and thus cost.

Best Practices & Cost Optimization:

  • Right-size Requests and Limits: Profile your applications to understand their actual resource needs. Don't over-request, as it leads to needing more/larger VMs. Don't under-request, as it can lead to performance issues or Pods not being scheduled.

  • Use Horizontal Pod Autoscaler (HPA): Scale the number of Pod replicas up or down based on metrics like CPU/memory utilization or custom metrics.

  • Use Cluster Autoscaler: Automatically adjusts the number of nodes in your node pools. If HPA scales up Pods and there's no room, Cluster Autoscaler adds nodes. If nodes are underutilized, it removes them.

  • Choose Appropriate VM SKUs: Balance cost and performance. Sometimes, a few larger VMs are more cost-effective than many small ones, or vice-versa, depending on your workload profile and overhead. Consider memory-optimized, compute-optimized, or general-purpose SKUs.

  • Utilize Azure Spot Virtual Machines: For fault-tolerant workloads, Spot VMs can significantly reduce costs but can be evicted with little notice. Use them in a separate node pool.

  • Monitor Utilization: Use Azure Monitor for containers or tools like Prometheus/Grafana to continuously track Pod and Node resource utilization. This helps identify optimization opportunities.

By understanding this mapping, you can make informed decisions about your Pod resource configurations and node pool choices to balance performance, reliability, and cost in your AKS deployments.

Resources:

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