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Cluster Autoscaler Monitoring

Introduction

Currently the options to monitor Cluster Autoscaler are limited to logs, status configmap and events. While useful for debugging, none of this options is particularly practical for monitoring Cluster Autoscaler behaviour over time. This document describes a set of metrics (in Prometheus format) that will be added to 0.6 Cluster Autoscaler to allow better monitoring of its operations.

Current state

Cluster Autoscaler already has a metrics endpoint providing some basic metrics. This includes default process metrics (number of goroutines, gc duration, cpu and memory details, etc) as well as some custom metrics related to time taken by various parts of Cluster Autoscaler main loop. Existing metrics will be renamed and refactored and new metrics will be added.

Metrics

Cluster Autoscaler needs to monitor some aspects of cluster state as part of its normal operations. It can therefore provide metrics describing its own operations as well as some metrics related to general state of the cluster.

All the metrics are prefixed with cluster_autoscaler_.

Cluster state

Metric name Metric type Labels Description
cluster_safe_to_autoscale Gauge Whether or not cluster is healthy enough for autoscaling. 1 if it is, 0 otherwise.
nodes_count Gauge state=<node-state> Number of nodes in cluster.
unschedulable_pods_count Gauge Number of unschedulable ("Pending") pods in the cluster.
node_groups_count Gauge node_group_type=<node-group-type> Number of node groups managed by CA.
  • cluster_safe_to_autoscale indicates whether cluster is healthy enough for autoscaling. CA stops all operations if significant number of nodes are unready (by default 33% as of CA 0.5.4).
  • nodes_count records the total number of nodes, labeled by node state. Possible states are ready, unready, notStarted.
  • node_groups_count records the number of currently managed node groups. It's useful when using dynamic configuration or Node Autoprovisioning. Types of node group are autoscaled (managed by CA but not created by NAP) and autoprovisioned (created by NAP and managed by CA).

Cluster Autoscaler execution

This metrics are refactored from currently existing metrics and track execution of various parts of Cluster Autoscaler loop.

Metric name Metric type Labels Description
last_activity Gauge activity=<autoscaler-activity> Last time certain part of CA logic executed
function_duration_seconds Histogram function=<autoscaler-function> Time taken by various parts of CA main loop.
  • last_activity records last time certain part of cluster autoscaler logic executed. Represented with unix timestamp. autoscaler-activity values are:

    • main - main loop iteration started.
    • autoscaling - current state of the cluster has been updated, started autoscaling logic.
    • scaleUp - autoscaler will check if scale up is necessary.
    • scaleDown - autoscaler will try to scale down some nodes.

    Not all of the above have to happen in every loop. For example if CA adds a new node it will completely skip scale down logic in this loop.

  • function_duration_seconds summarizes time taken by different functions executed in main CA goroutine. Uses the following set of values for autoscaler-function:

    • main - duration of the whole iteration of main loop.
    • updateClusterState - time used by CA to get node status from API server and update internal data structures.
    • scaleUp - time used to check if new node are necessary and add them.
    • findUnneeded - time required to find nodes that are candidates for removal.
    • scaleDown - time required to verify unneeded nodes are really unnecessary and remove them.

New labels may be added to both last_activity and function_duration_seconds if we add more features or additional logic to Cluster Autoscaler.

Cluster Autoscaler operations

This metrics describe internal state and actions taken by Cluster Autoscaler.

Metric name Metric type Labels Description
errors_total Counter type=<error-type> The number of CA loops failed due to an error.
scaled_up_nodes_total Counter Number of nodes added by CA.
scaled_down_nodes_total Counter reason=<scale-down-reason> Number of nodes removed by CA.
scaled_up_gpu_nodes_total Counter gpu_name=<gpu-name> Number of GPU-enabled nodes added by CA.
scaled_down_gpu_nodes_total Counter reason=<scale-down-reason>, gpu_name=<gpu-name> Number of GPU-enabled nodes removed by CA.
failed_scale_ups_total Counter reason=<failure-reason> Number of times scale-up operation has failed.
evicted_pods_total Counter Number of pods evicted by CA.
unneeded_nodes_count Gauge Number of nodes currently considered unneeded by CA.
  • errors_total counter increases every time main CA loop encounters an error.
    • Growing errors_total count signifies an internal error in CA or a problem with underlying infrastructure preventing normal CA operation. Example errors include:
      • failed to get list of nodes or pods from API server,
      • failed to retrieve node group size from cloud provider,
      • failed to update node group size,
      • error in CA simulations,
      • error updating internal data structures.
    • Not every condition causing CA to skip part of main loop is an error in this context. For example having pending pods after reaching maximum cluster size causes ScaleUp function to return error resulting in CA skipping the rest of main loop. This is expected behaviour of CA and it should not be counted as error.
    • Possible error types are:
      • cloudProviderError - failed to get node group info, set node group size, etc.
      • apiCallError - error related to call to k8s api server (ex. get nodes, get pods).
      • internalError - error in any other part of CA logic.
  • scaled_up_nodes_total counts the number of nodes successfully added by CA. In this context we consider node as successfully added after updating node group size (without waiting for actual vm to spin up, run a kubelet, etc).
  • failed_scale_ups_total counts the number of unsuccessful scale-up operations performed by CA. This includes both getting error from cloud provider and new nodes failing to boot up and register within timeout. It does not include reaching maximum cluster size (as CA doesn't attempt scale-up at all in that case).
  • scaled_down_nodes_total counts the number of nodes removed by CA. Possible scale down reasons are empty, underutilized, unready.
  • scaled_up_gpu_nodes_total counts the number of GPU-enabled nodes successfully added by CA, similar to scaled_up_nodes_total. Additionally gpu_name specifies name of the GPU (e.g. nvidia-tesla-k80).
  • scaled_down_gpu_nodes_total counts the number of nodes removed by CA. Scale down reasons are identical to scaled_down_nodes_total, gpu_name to scaled_up_gpu_nodes_total.

Node Autoprovisioning operations

This metrics describe operations and state related to Node Autoprovisioning feature.

Metric name Metric type Labels Description
nap_enabled Gauge Whether or not Node Autoprovisioning is enabled. 1 if it is, 0 otherwise.
created_node_groups_total Counter Number of node groups created by Node Autoprovisioning.
deleted_node_groups_total Counter Number of node groups deleted by Node Autoprovisioning.
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