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[DOCS] Reviews ML decider conceptual docs (elastic#69524)
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Co-authored-by: Lisa Cawley <[email protected]>
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szabosteve and lcawl committed Mar 1, 2021
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[[autoscaling-machine-learning-decider]]
=== Machine learning decider

The {ml} decider (`ml`) calculates the memory required to run
{ml} jobs created by users.
The {ml} decider (`ml`) calculates the memory required to run {ml} jobs.

The {ml} decider is enabled for policies governing `ml` nodes.

NOTE: For {ml} jobs to open when the cluster is not appropriately
scaled, `xpack.ml.max_lazy_ml_nodes` should be set to the largest
number of possible {ml} jobs (see <<advanced-ml-settings>>). In
{ess} this is already handled.
scaled, set `xpack.ml.max_lazy_ml_nodes` to the largest number of possible {ml}
jobs (refer to <<advanced-ml-settings>> for more information). In {ess}, this is
automatically set.

[[autoscaling-machine-learning-decider-settings]]
==== Configuration settings

Both `num_anomaly_jobs_in_queue` and `num_analytics_jobs_in_queue`
are designed to be used to delay a scale-up event. They indicate how many jobs
of that type can be unassigned from a node due to the cluster being
too small. Both settings are only considered for jobs that could
eventually be fully opened given the current scale. If a job is too
large for any node size or if a job couldn't ever be assigned without
user intervention (for example, a user calling `_stop` against a real-time
Both `num_anomaly_jobs_in_queue` and `num_analytics_jobs_in_queue` are designed
to delay a scale-up event. If the cluster is too small, these settings indicate how many jobs of each type can be
unassigned from a node. Both settings are
only considered for jobs that can be opened given the current scale. If a job is
too large for any node size or if a job can't be assigned without user
intervention (for example, a user calling `_stop` against a real-time
{anomaly-job}), the numbers are ignored for that particular job.

`num_anomaly_jobs_in_queue`::
(Optional, integer)
Number of queued anomaly jobs to allow. Defaults to `0`.
Specifies the number of queued {anomaly-jobs} to allow. Defaults to `0`.

`num_analytics_jobs_in_queue`::
(Optional, integer)
Number of queued analytics jobs to allow. Defaults to `0`.
Specifies the number of queued {dfanalytics-jobs} to allow. Defaults to `0`.

`down_scale_delay`::
(Optional, <<time-units,time value>>)
Delay before scaling down. Defaults to 1 hour. If a scale down is possible
for the entire time window, then a scale down is requested. If the cluster
requires a scale up during the window, the window is reset.
Specifies the time to delay before scaling down. Defaults to 1 hour. If a scale
down is possible for the entire time window, then a scale down is requested. If
the cluster requires a scale up during the window, the window is reset.


[[autoscaling-machine-learning-decider-examples]]
==== {api-examples-title}

This example puts an autoscaling policy named `my_autoscaling_policy`,
overriding the machine learning decider's configuration.
This example creates an autoscaling policy named `my_autoscaling_policy` that
overrides the default configuration of the {ml} decider.

[source,console]
--------------------------------------------------
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--------------------------------------------------
// TEST


The API returns the following result:

[source,console-result]
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}
--------------------------------------------------


//////////////////////////
[source,console]
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