Configuration for Central Managers
==================================
:index:`central manager<single: central manager; machine>`

Introduction
------------

The Central Manager machine, or CM, is a role that usually runs on one machine
in your pool.  In some ways, it defines the extent and scope of the pool.  A
central manager must run at least one *condor_collector* and
*condor_negotiator* daemon, and most pools run exactly one of each.  Some large
sites run multiple collectors, perhaps on multiple machines, to handle the load
over a very large pool (say, more than 100,000 slots), or to move the load of
handling monitoring and reporting queries to a different collector than is
handling the operational aspects of running a pool, or to improve the
availability of the pool as a whole.  In the same way, most pools run with a
single negotiator daemon, but in some specialized configurations,
separate negotiators can handle fast matching to a subset of the pool, or break
the pool accounting into separate shards.

.. sidebar::
   Central Manager (CM) Diagram

   .. mermaid::
      :caption: Daemons for Central Manager, both managed by a :tool:`condor_master`
      :align: center

      flowchart TD
         condor_master --> condor_collector & condor_negotiator

The *condor_collector* is mainly an in-memory datastore of classads representing 
all of the daemons and all of the slots in the pool.  Because it keeps this in memory,
all state of the pool's daemons is lost when it restarts.  To mitigate problems with
restarts, each daemon is responsible to periodically send the current value of its
classad to the collector, usually, no less frequently than every five minutes.  Thus
even after a restart, the whole state of the pool will refresh within five minutes.
Most of the various options to the :tool:`condor_status` command query the *condor_collector*.
There is not much configuration for the *condor_collector* daemon.

The *condor_negotiator*, is responsible for allocating the slots of the pools
to users according to the policies of the administrators of the CM and the EP.
Thus, there is a lot of configuration control that you have over this daemon
and how it works.  Scheduling of jobs is a two-phased process.  The *condor_negotiator*
tries to balance the allocation of all the slots in the pool in some fair way.
It then gives these allocated slots to the *condor_schedd*'s that hold the jobs
of those users, and it is the *condor_schedd* on the AP that is responsible for the
final selection of which jobs run on which slots.  The *condor_schedd* can re-use a
match the negotiator has given it to run multiple jobs in succession.  Because of this,
the negotiator isn't as time-critical for starting jobs as the *condor_schedd* might be,
so it can afford to take more time to make better decisions.  The *condor_negotiator*
has a pool-wide view of scheduling, but the *condor_schedd* can only see those slots
in the pool that have been assigned to it.

Configuration and Operation of the Negotiator
---------------------------------------------

The Negotiation Cycle
'''''''''''''''''''''

:index:`negotiation`
:index:`negotiation cycle<single: negotiation cycle; matchmaking>`

The heart of the *condor_negotiator* is the negotiation cycle.  The
*condor_negotiator* runs the negotiation cycle, a cpu-bound process which can
run for minutes, then waits for :macro:`NEGOTIATOR_CYCLE_DELAY` before starting
the next cycle.  During this delay, the rest of the system processes the
decisions made by the negotiator, and the negotiator can also respond to
queries made by :tool:`condor_userprio` and other tools.  The job of 
the negotiation cycle is to match slots to submitters and their jobs.

.. sidebar:: The term "Submitter"

   For accounting and fair-share purposes, HTCondor needs to know who
   to "charge" for usage, and who the user is.  Some systems call this
   the "accounting principal".  In HTCondor, we call this entity "the
   submitter".  By default, the submitter is the same as the operating
   system users, but it need not be.  For example, if the same human
   has an account on two different APs, and can submit jobs from either of
   those, should those two OS accounts be considered the same for HTCondor
   fair-share and accounting?  The answer is the submitter -- if jobs
   from those two submissions come from the same HTCondor submitter,
   they are considered the same for HTCondor's accounting. Conversely,
   if a human is part of two projects, say, Chemistry and Genetics, perhaps
   an HTCondor administrator would like to consider those differently 
   for accounting reasons.  If the user submits the jobs with different
   submitters, using :subcom:`accounting_group_user`, they will be
   considered different.  This also comes up in submissions from web portals,
   where the portal may be running as one OS users, and submitting jobs
   on behalf of many different humans, who should be considered as
   different accounting entries.


#. Build a list of all the slots, subject to :macro:`NEGOTIATOR_SLOT_CONSTRAINT`
   This is the equivalent of running :tool:`condor_status`.  If
   :macro:`NEGOTIATOR_CONSIDER_PREEMPTION` is false, only use idle slots.
   This means preemption can't happen, but negotiation will be much faster.
#. Obtain a list of all job submitters
   This is the equivalent of running :tool:`condor_status` -submitters.
#. Sort the list of all job submitters based on the Effective User Priority (EUP) (see
   below for an explanation of EUP). The submitter with the lowest numerical priority is first.
   The command :tool:`condor_userprio` shows the EUP for each submitter.
#. Calculate the "fair share" for each submitter, which is the number of cores
   each submitter should be getting, and the actual number of cores each
   submitter is using.  Note that as some submitters may have less demand for
   slots than their fair share, these excess slots might be allocated to other
   submitters, putting them over their fair share.

#. Iterate until the negotiator runs out of slots or jobs.

       For each submitter below their floor, if any; after those, for all 
       submitters, both in EUP order:

           Fetch an idle job from a *condor_schedd* holding jobs from
           that submitter.  The *condor_schedd* will present the jobs
           to the negotiator in job :subcom:`priority` order.

           -  Build a potential match list of slots where this job could run,
              out of all the slots the negotiator fetched in the first step above:

              #. Skip slots where ``machine.requirements`` evaluates to ``False`` or
                 ``job.requirements`` evaluates to ``False``.  That is, skip the slot 
                 if it does not match the job's requirements or vice-versa.
              #. Skip slots in the Claimed state, but not running a job.
              #. For slots not running a job, add to the
                 potential match list with a reason of No Preemption.
              #. For slots running a job

                 -  If the ``machine.RANK`` on this job is better than
                    the running job, add the slot to the potential
                    match list with a reason of Rank.
                 -  If the EUP of this job is better than the EUP of the
                    currently running job, and
                    :macro:`PREEMPTION_REQUIREMENTS` is ``True``, and the
                    ``machine.RANK`` on the job is not worse than the
                    currently running job, add the slot to the
                    potential match list with a reason of Priority.
                    See example below.

           -  Sort the potential match list, first by
              :macro:`NEGOTIATOR_PRE_JOB_RANK`. If two or more slots
              have the same value, sort those by ``job.RANK``.  Break
              any more ties by sorting by :macro:`NEGOTIATOR_POST_JOB_RANK`.
              Further ties are broken by reason for claim (No Preemption, 
              then Rank, then Priority), and finally :macro:`PREEMPTION_RANK`.
           -  The job is assigned to the top slot on the potential
              match list. The slot is then removed from the list of
              slots to match (on this negotiation cycle).
           -  Ask the *condor_schedd* for the next idle job for this submitter.
              If there are no more idle jobs, move to the next submitter.

As described above, the *condor_negotiator* tries to match each job
to all slots in the pool.  Assume that five slots match one request for
three jobs, and that their :macro:`NEGOTIATOR_PRE_JOB_RANK`, ``Job.Rank``, 
and :macro:`NEGOTIATOR_POST_JOB_RANK` expressions evaluate (in the context 
of both the slot ad and the job ad) to the following values.

+------------+-------------------------+----------+-------------------------+
|Slot Name   |  NEGOTIATOR_PRE_JOB_RANK|  Job.Rank| NEGOTIATOR_POST_JOB_RANK|
+============+=========================+==========+=========================+
|slot1       |                      100|         1|                       10|
+------------+-------------------------+----------+-------------------------+
|slot2       |                      100|         2|                       20|
+------------+-------------------------+----------+-------------------------+
|slot3       |                      100|         2|                       30|
+------------+-------------------------+----------+-------------------------+
|slot4       |                        0|         1|                       40|
+------------+-------------------------+----------+-------------------------+
|slot5       |                      200|         1|                       50|
+------------+-------------------------+----------+-------------------------+

Table 3.1: Example of slots in the potential match list before sorting

These slots would be sorted first on :macro:`NEGOTIATOR_PRE_JOB_RANK`, then
sorting all ties based on ``Job.Rank`` and any remaining ties sorted by
:macro:`NEGOTIATOR_POST_JOB_RANK`.  After that, the first three slots would be
handed to the *condor_schedd*.  This means that
:macro:`NEGOTIATOR_PRE_JOB_RANK` is very strong, and overrides any ranking
expression by the submitter of the job.  After sorting, the slots would look
like this, and the schedd would be given slot5, slot3 and slot2:

+-------------+-------------------------+----------+-------------------------+
| Slot Name   | NEGOTIATOR_PRE_JOB_RANK | Job.Rank | NEGOTIATOR_POST_JOB_RANK|
+=============+=========================+==========+=========================+
| slot5       |                      200|         1|                       50|
+-------------+-------------------------+----------+-------------------------+
| slot3       |                      100|         2|                       30|
+-------------+-------------------------+----------+-------------------------+
| slot2       |                      100|         2|                       20|
+-------------+-------------------------+----------+-------------------------+
| slot1       |                      100|         1|                       10|
+-------------+-------------------------+----------+-------------------------+
| slot4       |                        0|         1|                       40|
+-------------+-------------------------+----------+-------------------------+

Table 3.2: Example of slots in the potential match list after sorting: Here, slot5 would be the first slot given to a submitter.


The *condor_negotiator* asks the *condor_schedd* for the "next job" from a
given submitter/user. Typically, the *condor_schedd* returns jobs in the order
of job priority. If priorities are the same, job submission time is used; older
jobs go first. If a cluster has multiple procs in it and one of the jobs cannot
be matched, the *condor_schedd* will not return any more jobs in that cluster
on that negotiation pass.  This is an optimization based on the theory that the
cluster jobs are similar. The configuration variable
:macro:`NEGOTIATE_ALL_JOBS_IN_CLUSTER` disables the cluster-skipping
optimization. Use of the configuration variable :macro:`SIGNIFICANT_ATTRIBUTES`
will change the definition of what the *condor_schedd* considers a cluster from
the default definition of all jobs that share the same :ad-attr:`ClusterId`.

User Priorities and Negotiation
'''''''''''''''''''''''''''''''

:index:`in machine allocation<single: in machine allocation; priority>`
:index:`user priority`

HTCondor uses priorities to allocate slots to jobs. This
section details the priorities and the allocation of slots
(negotiation).

.. note::
    A video describing how user priorities, negotiation and
    fair share work in HTCondor is available at 
    https://www.youtube.com/watch?v=NNnrCjFV0tM

For accounting purposes, each user is identified by
username@uid_domain. Each user is assigned a priority value even if
submitting jobs from different Access Points in the same domain, or even if
submitting from multiple machines in the different domains.

The numerical priority value assigned to a user is inversely related to
the goodness of the priority. A user with a numerical priority of 5 gets
more slots (or bigger slots) than a user with a numerical priority of 50. There are
two priority values assigned to HTCondor users:

-  Real User Priority (RUP), which measures resource usage of the user.
-  Effective User Priority (EUP), which determines the number of
   slots the user can get.

This section describes these two priorities and how they affect resource
allocations in HTCondor. Documentation on configuring and controlling
priorities may be found in the 
:ref:`negotiator_config_options` section.

Real User Priority (RUP)
''''''''''''''''''''''''

:index:`real user priority (RUP)`
:index:`real (RUP)<single: real (RUP); user priority>`

A user's RUP reports a smoothed average of the number of cores a user
has used over some recent period of time. Every user begins with a RUP of 
one half (0.5), which is the lowest possible value. At steady state, the RUP
of a user equilibrates to the number of cores currently used.
So, if a specific user continuously uses exactly ten cores
for a long period of time, the RUP of that user asymptotically 
approaches ten.

However, if the user decreases the number of cores used, the RUP asymptotically
lowers to the new value. The rate at which the priority value decays can be set
by the macro :macro:`PRIORITY_HALFLIFE`, a time period defined in seconds.
Intuitively, if the :macro:`PRIORITY_HALFLIFE` in a pool is set to the default
of 86400 seconds (one day), and a user with a RUP of 10 has no running jobs,
that user's RUP would be 5 one day later, 2.5 two days later, and so on.

For example, if a new user has no historical usage, their RUP will start 
at 0.5  If that user then has 100 cores running, their RUP will grow
as the graph below show:

.. figure:: /_images/user-prio1.png
    :width: 1600
    :alt: User Priority
    :align: center

Or, if a new user with no historical usage has 100 cores running
for 24 hours, then removes all the jobs, so has no cores running, 
their RUP will grow and shrink as shown below:

.. figure:: /_images/user-prio2.png
    :width: 1600
    :alt: User Priority
    :align: center

Effective User Priority (EUP)
'''''''''''''''''''''''''''''

:index:`effective user priority (EUP)`
:index:`effective (EUP)<single: effective (EUP); user priority>`

The effective user priority (EUP) of a user is used to determine how many cores
a user should receive. The EUP is simply the RUP multiplied by a priority
factor the administrator can set per-user.  The default initial priority factor
for all new users as they first submit jobs is set by the configuration
variable :macro:`DEFAULT_PRIO_FACTOR`, and defaults to 1000.0. An administrator
can change this priority factor using the :tool:`condor_userprio` command.  For
example, setting the priority factor of some user to 2,000 will grant that user
half as many cores as a user with the default priority factor of 1,000,
assuming they both have the same historical usage, because their EUPs will
differ by a factor of two.

The number of slots that a user may receive is inversely related to
the ratio between the EUPs of submitting users. User A with
EUP=5 will receive twice as many slots as user B with EUP=10 and
four times as many slots as user C with EUP=20. However, if A does
not use the full number of slots that A may be given, the available
slots are repartitioned and distributed among remaining users
according to the inverse ratio rule.

Assume two users with no history, named A and B, using a pool with 100 cores. To
simplify the math, also assume both users have an equal priority factor of 1.0.
User A submits a very large number of short-running jobs at time t = 0.  User
B waits until 48 hours later, and also submits an infinite number of short jobs.
At the beginning, the EUP doesn't matter, as there is only one user with jobs, 
and so user A gets the whole pool.  At the 48 hour mark, both users compete for
the pool.  Assuming the default :macro:`PRIORITY_HALFLIFE` of 24 hours, user A's RUP
should be about 75.0 at the 48 hour mark, and User B will still be the minimum of
.5.  At that instance, User B deserves 150 times User A.  However, this ratio will
decay quickly.  User A's share of the pool will drop from all 100 cores to less than
one core immediately, but will quickly rebound to a handful of cores, and will 
asymptotically approach half of the pool as User B gets the inverse. A graph
of these two users might look like this:

.. figure:: /_images/fair-share.png
    :width: 1600
    :alt: Fair Share
    :align: center



HTCondor supplies mechanisms to directly support two policies in which
EUP may be useful:

Nice users
    A job may be submitted with the submit command
    :subcom:`nice_user[impact on prio]` set to
    ``True``. This nice user job will have its RUP boosted by the
    :macro:`NICE_USER_PRIO_FACTOR`
    priority factor specified in the configuration, leading to a very
    large EUP. This corresponds to a low priority for slots,
    therefore using slots not used by other HTCondor users.

Remote Users
    HTCondor's flocking feature (see the :doc:`/grid-computing/connecting-pools-with-flocking` section)
    allows jobs to run in a pool other than the local one. In addition,
    the submit-only feature allows a user to submit jobs to another
    pool. In such situations, submitters from other domains can submit
    to the local pool. It may be desirable to have HTCondor treat local
    users preferentially over these remote users. If configured,
    HTCondor will boost the RUPs of remote users by
    :macro:`REMOTE_PRIO_FACTOR` specified
    in the configuration, thereby lowering their priority for slots.

The priority boost factors for individual users can be set with the
**setfactor** option of :tool:`condor_userprio`. Details may be found in the
:doc:`/man-pages/condor_userprio` manual page.

Priorities in Negotiation and Preemption
''''''''''''''''''''''''''''''''''''''''

:index:`priority<single: priority; negotiation>` :index:`priority<single: priority; matchmaking>`
:index:`priority<single: priority; preemption>`

Priorities are used to ensure that users get their fair share of
slots in the pool. The priority values are used at allocation time, meaning
during negotiation and matchmaking. Therefore, there are ClassAd
attributes that take on defined values only during negotiation, making
them ephemeral. In addition to allocation, HTCondor may preempt a
slot claim and reallocate it when conditions change.

Too many preemptions lead to thrashing, a condition in which negotiation
for a slot identifies a new job with a better priority most every
cycle. Each job is, in turn, preempted, and no job finishes. To avoid
this situation, the :macro:`PREEMPTION_REQUIREMENTS` configuration variable is defined
for and used only by the *condor_negotiator* daemon to specify the
conditions that must be met for a preemption to occur. When preemption
is enabled, it is usually defined to deny preemption if a current
running job has been running for a relatively short period of time. This
effectively limits the number of preemptions per resource per time
interval. Note that :macro:`PREEMPTION_REQUIREMENTS` only applies to
preemptions due to user priority. It does not have any effect if the
slot's :macro:`RANK` expression prefers a different job, or if the
slot's policy causes the job to vacate due to other activity on the
slot. See the :ref:`admin-manual/ep-policy-configuration:*condor_startd* policy
configuration` section for the current default policy on preemption.

The following ephemeral attributes may be used within policy
definitions. Care should be taken when using these attributes, due to
their ephemeral nature; they are not always defined, so the usage of an
expression to check if defined such as

.. code-block:: condor-classad-expr

      (RemoteUserPrio =?= UNDEFINED)

is likely necessary.

Within these attributes, those with names that contain the string ``Submitter``
refer to characteristics about the candidate job's user; those with names that
contain the string ``Remote`` refer to characteristics about the user currently
using the resource. Further, those with names that end with the string
``ResourcesInUse`` have values that may change within the time period
associated with a single negotiation cycle. Therefore, the configuration
variables :macro:`PREEMPTION_REQUIREMENTS_STABLE` and
:macro:`PREEMPTION_RANK_STABLE` exist to inform the *condor_negotiator* daemon
that values may change. See the
:ref:`negotiator_config_options` section for definitions of these configuration variables.


:index:`SubmitterUserPrio<single: SubmitterUserPrio; ClassAd attribute, ephemeral>`\ ``SubmitterUserPrio``
    A floating point value representing the user priority of the
    candidate job.

:index:`SubmitterUserResourcesInUse<single: SubmitterUserResourcesInUse; ClassAd attribute, ephemeral>`\ ``SubmitterUserResourcesInUse``
    The integer number of slots currently utilized by the user
    submitting the candidate job.

:index:`RemoteUserPrio<single: RemoteUserPrio; ClassAd attribute, ephemeral>`\ ``RemoteUserPrio``
    A floating point value representing the user priority of the job
    currently running on the slot. This version of the attribute,
    with no slot represented in the attribute name, refers to the
    current slot being evaluated.

:index:`Slot_RemoteUserPrio<single: Slot_RemoteUserPrio; ClassAd attribute, ephemeral>`\ ``Slot<N>_RemoteUserPrio``
    A floating point value representing the user priority of the job
    currently running on the particular slot represented by <N> on the
    slot.

:index:`RemoteUserFloor<single: RemoteUserFloor; ClassAd attribute, ephemeral>` \ ``RemoteUserFloor``
    An integer value representing the resource/slot floor (minimum
    number of slots guaranteed to the user) for the job currently
    running on the slot. This version of the attribute, with no slot
    represented in the attribute name, refers to the current slot being
    evaluated.

:index:`RemoteUserResourcesInUse<single: RemoteUserResourcesInUse; ClassAd attribute, ephemeral>`\ ``RemoteUserResourcesInUse``
    The integer number of slots currently utilized by the user of the
    job currently running on the slot.

:index:`SubmitterGroupResourcesInUse<single: SubmitterGroupResourcesInUse; ClassAd attribute, ephemeral>`\ ``SubmitterGroupResourcesInUse``
    If the owner of the candidate job is a member of a valid accounting
    group, with a defined group quota, then this attribute is the
    integer number of slots currently utilized by the group.

:index:`SubmitterGroup<single: SubmitterGroup; ClassAd attribute, ephemeral>`\ :ad-attr:`SubmitterGroup`
    The accounting group name of the requesting submitter.

:index:`SubmitterGroupQuota<single: SubmitterGroupQuota; ClassAd attribute, ephemeral>`\ ``SubmitterGroupQuota``
    If the owner of the candidate job is a member of a valid accounting
    group, with a defined group quota, then this attribute is the
    integer number of slots defined as the group's quota.

:index:`RemoteGroupResourcesInUse<single: RemoteGroupResourcesInUse; ClassAd attribute, ephemeral>`\ ``RemoteGroupResourcesInUse``
    If the owner of the currently running job is a member of a valid
    accounting group, with a defined group quota, then this attribute is
    the integer number of slots currently utilized by the group.

:index:`RemoteGroup<single: RemoteGroup; ClassAd attribute, ephemeral>`\ :ad-attr:`RemoteGroup`
    The accounting group name of the owner of the currently running job.

:index:`RemoteGroupQuota<single: RemoteGroupQuota; ClassAd attribute, ephemeral>`\ ``RemoteGroupQuota``
    If the owner of the currently running job is a member of a valid
    accounting group, with a defined group quota, then this attribute is
    the integer number of slots defined as the group's quota.

:index:`SubmitterNegotiatingGroup<single: SubmitterNegotiatingGroup; ClassAd attribute, ephemeral>`\ :ad-attr:`SubmitterNegotiatingGroup`
    The accounting group name that the candidate job is negotiating
    under.

:index:`RemoteNegotiatingGroup<single: RemoteNegotiatingGroup; ClassAd attribute, ephemeral>`\ :ad-attr:`RemoteNegotiatingGroup`
    The accounting group name that the currently running job negotiated
    under.

:index:`SubmitterAutoregroup<single: SubmitterAutoregroup; ClassAd attribute, ephemeral>`\ :ad-attr:`SubmitterAutoregroup`
    Boolean attribute is ``True`` if candidate job is negotiated via
    autoregroup.

:index:`RemoteAutoregroup<single: RemoteAutoregroup; ClassAd attribute, ephemeral>`\ :ad-attr:`RemoteAutoregroup`
    Boolean attribute is ``True`` if currently running job negotiated
    via autoregroup.

Priority Calculation
''''''''''''''''''''

This section may be skipped if the reader so feels, but for the curious,
here is HTCondor's priority calculation algorithm.

The RUP of a user :math:`u` at time :math:`t`, :math:`\pi_{r}(u,t)`, is calculated every
time interval :math:`\delta t` using the formula

.. math::

    \pi_r(u,t) = \beta × \pi_r(u, t - \delta t) + (1 - \beta) × \rho(u, t)

where :math:`\rho (u,t)` is the number of cores used by user :math:`u` at time :math:`t`,
and :math:`\beta = 0.5^{\delta t / h}`.
:math:`h` is the half life period set by :macro:`PRIORITY_HALFLIFE`.

The EUP of user :math:`u` at time :math:`t`, :math:`\pi_{e}(u,t)` is calculated by

.. math::

    \pi_e(u,t) = \pi_r(u,t) \times f(u,t)

where :math:`f(u,t)` is the priority boost factor for user :math:`u` at time :math:`t`.

As mentioned previously, the RUP calculation is designed so that at
steady state, each user's RUP stabilizes at the number of cores used
by that user. The definition of :math:`\beta` ensures that the calculation of
:math:`\pi_{r}(u,t)` can be calculated over non-uniform time intervals :math:`\delta t`
without affecting the calculation. The time interval :math:`\delta t` varies due to
events internal to the system, but HTCondor guarantees that unless the
central manager is down, no matches will be unaccounted for due
to this variance.

The Layperson's Description of the Pie Spin and Pie Slice
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''

:index:`pie slice` :index:`pie spin`
:index:`pie slice<single: pie slice; scheduling>`
:index:`pie spin<single: pie spin; scheduling>`

The negotiator first finds all users who
have submitted jobs and calculates their priority. Then, it totals the
SlotWeight (by default, cores) of all currently available slots, and 
using the ratios of the user priorities, it calculates the number of 
cores each user could get. This is their pie slice.
(See: SLOT_WEIGHT in :ref:`startd_config_options`)

If any users have a floor defined via :tool:`condor_userprio` -set-floor
, and their current allocation of cores is below the floor, a 
special round of the below-floor users goes first, attempting to 
allocate up to the defined number of cores for their floor level.  
These users are negotiated for in user priority order.  This allows
an admin to give users some "guaranteed" minimum number of cores, no
matter what their previous usage or priority is.

After the below-floor users are negotiated for, all users
are negotiated for, in user priority order. 
The *condor_negotiator* contacts each schedd where the user's job lives, and asks for job 
information. The *condor_schedd* daemon (on behalf of
a user) tells the matchmaker about a job, and the matchmaker looks at
available slots to create a list that match the requirements expression. 
It then sorts the matching slots by the rank expressions within ClassAds. 
If a slot prefers a job via the slot RANK expression, the job 
is assigned to that slot, potentially preempting an already running job.
Otherwise, give the slot to the job that the job ranks highest. If
the highest ranked slot is already running a job, the negotiator may preempt
the running job for the new job. 

This matchmaking cycle continues until the user has received all of the
slots for their pie slice. If there is a per-user ceiling defined
with the :tool:`condor_userprio` -setceil command, and this ceiling is smaller
than the pie slice, the user gets only up to their ceiling number of
cores.  The matchmaker then contacts the next
highest priority user and offers that user their pie slice worth of
slots. After contacting all users, the cycle is repeated with any
still available slots and recomputed pie slices. The matchmaker
continues spinning the pie until it runs out of slots or all the
*condor_schedd* daemons say they have no more jobs.

.. _Group Accounting:

Group Accounting
''''''''''''''''

:index:`accounting<single: accounting; groups>` :index:`by group<single: by group; accounting>`
:index:`by group<single: by group; priority>`

By default, HTCondor does all accounting on a per-user basis. 
This means that HTCondor keeps track of the historical usage per-user,
calculates a priority and fair-share per user, and allows the 
administrator to change this fair-share per user.  In HTCondor
terminology, the accounting principal is called the submitter.

The name of this submitter is, by default, the name the schedd authenticated
when the job was first submitted to the schedd.  Usually, this is
the operating system username.  However, the submitter can override
the username selected by setting the submit file option

.. code-block:: condor-submit

    accounting_group_user = ishmael

This means this job should be treated, for accounting purposes only, as
"ishmael", but "ishmael" will not be the operating system id the shadow
or job uses.  Note that HTCondor trusts the user to set this
to a valid value.  The administrator can use schedd requirements or transforms
to validate such settings, if desired.  accounting_group_user is frequently used
in web portals, where one trusted operating system process submits jobs on
behalf of different users.

Note that if many people submit jobs with identical accounting_group_user values,
HTCondor treats them as one set of jobs for accounting purposes.  So, if
Alice submits 100 jobs as accounting_group_user ishmael, and so does Bob
a moment later, HTCondor will not try to fair-share between them, 
as it would do if they had not set accounting_group_user.  If all these 
jobs have identical requirements, they will be run First-In, First-Out, 
so whoever submitted first makes the subsequent jobs wait until the 
last one of the first submit is finished.

.. _Hierarchical Group Quotas:

Accounting Groups with Hierarchical Group Quotas
''''''''''''''''''''''''''''''''''''''''''''''''

:index:`hierarchical group quotas`
:index:`by group<single: by group; negotiation>` :index:`quotas<single: quotas; groups>`
:index:`hierarchical quotas for a group<single: hierarchical quotas for a group; quotas>`

With additional configuration, it is possible to create accounting
groups, where the submitters within the group maintain their distinct
identity, and fair-share still happens within members of that group.

An upper limit on the number of slots allocated to a group of users can
be specified with group quotas.

Consider an example pool with thirty slots: twenty slots are owned by
the physics group and ten are owned by the chemistry group. The desired
policy is that no more than twenty concurrent jobs are ever running from
the physicists, and only ten from the chemists. These slots are
otherwise identical, so it does not matter which slots run which
group's jobs. It only matters that the proportions of allocated slots
are correct.

Group quotas may implement this policy. Define the groups and set their
quotas in the configuration of the central manager:

.. code-block:: condor-config

    GROUP_NAMES = group_physics, group_chemistry
    GROUP_QUOTA_group_physics =   20
    GROUP_QUOTA_group_chemistry = 10

The implementation of quotas is hierarchical, such that quotas may be
described for the tree of groups, subgroups, sub subgroups, etc. Group
names identify the groups, such that the configuration can define the
quotas in terms of limiting the number of cores allocated for a group or
subgroup. Group names do not need to begin with ``"group_"``, but that
is the convention, which helps to avoid naming conflicts between groups
and subgroups. The hierarchy is identified by using the period ('.')
character to separate a group name from a subgroup name from a sub
subgroup name, etc. Group names are case-insensitive for negotiation.
:index:`<none> group`
:index:`<none> group<single: <none> group; group accounting>`

At the root of the tree that defines the hierarchical groups is the
"<none>" group. The implied quota of the "<none>" group will be
all available slots. This string will appear in the output of
:tool:`condor_status`.

If the sum of the child quotas exceeds the parent, then the child quotas
are scaled down in proportion to their relative sizes. For the given
example, there were 30 original slots at the root of the tree. If a
power failure removed half of the original 30, leaving fifteen slots,
physics would be scaled back to a quota of ten, and chemistry to five.
This scaling can be disabled by setting the *condor_negotiator*
configuration variable
:macro:`NEGOTIATOR_ALLOW_QUOTA_OVERSUBSCRIPTION` to ``True``. If
the sum of the child quotas is less than that of the parent, the child
quotas remain intact; they are not scaled up. That is, if somehow the
number of slots doubled from thirty to sixty, physics would still be
limited to 20 slots, and chemistry would be limited to 10. This example
in which the quota is defined by absolute values is called a static
quota.

Each job must state which group it belongs to. By default, this is opt-in,
and the system trusts each user to put the correct group in the submit
description file. See "Setting Accounting Groups Automatically below"
to configure the system to set them without user input and to prevent
users from opting into the wrong groups.  Jobs that do not identify 
themselves as a group member are negotiated for as part of the "<none>" 
group. Note that this requirement is per job, not per user. A given user 
may be a member of many groups. Jobs identify which group they are in by setting the
:subcom:`accounting_group[and negotiation]` and
:subcom:`accounting_group_user[and negotiation]`
commands within the submit description file, as specified in the
:ref:`admin-manual/cm-configuration:group accounting` section.
For example:

.. code-block:: condor-submit

    accounting_group = group_physics
    accounting_group_user = einstein

The size of the quotas may instead be expressed as a proportion. This is
then referred to as a dynamic group quota, because the size of the quota
is dynamically recalculated every negotiation cycle, based on the total
available size of the pool. Instead of using static quotas, this example
can be recast using dynamic quotas, with one-third of the pool allocated
to chemistry and two-thirds to physics. The quotas maintain this ratio
even as the size of the pool changes, perhaps because of machine
failures, because of the arrival of new machines within the pool, or
because of other reasons. The job submit description files remain the
same. Configuration on the central manager becomes:

.. code-block:: condor-config

    GROUP_NAMES = group_physics, group_chemistry
    GROUP_QUOTA_DYNAMIC_group_chemistry = 0.33
    GROUP_QUOTA_DYNAMIC_group_physics =   0.66

The values of the quotas must be less than 1.0, indicating fractions of
the pool's machines. As with static quota specification, if the sum of
the children exceeds one, they are scaled down proportionally so that
their sum does equal 1.0. If their sum is less than one, they are not
changed.

Extending this example to incorporate subgroups, assume that the physics
group consists of high-energy (hep) and low-energy (lep) subgroups. The
high-energy sub-group owns fifteen of the twenty physics slots, and the
low-energy group owns the remainder. Groups are distinguished from
subgroups by an intervening period character (.) in the group's name.
Static quotas for these subgroups extend the example configuration:

.. code-block:: condor-config

    GROUP_NAMES = group_physics, group_physics.hep, group_physics.lep, group_chemistry
    GROUP_QUOTA_group_physics     =   20
    GROUP_QUOTA_group_physics.hep =   15
    GROUP_QUOTA_group_physics.lep =    5
    GROUP_QUOTA_group_chemistry   =   10

This hierarchy may be more useful when dynamic quotas are used. Here is
the example, using dynamic quotas:

.. code-block:: condor-config

      GROUP_NAMES = group_physics, group_physics.hep, group_physics.lep, group_chemistry
      GROUP_QUOTA_DYNAMIC_group_chemistry   =   0.33334
      GROUP_QUOTA_DYNAMIC_group_physics     =   0.66667
      GROUP_QUOTA_DYNAMIC_group_physics.hep =   0.75
      GROUP_QUOTA_DYNAMIC_group_physics.lep =   0.25

The fraction of a subgroup's quota is expressed with respect to its
parent group's quota. That is, the high-energy physics subgroup is
allocated 75% of the 66% that physics gets of the entire pool, however
many that might be. If there are 30 machines in the pool, that would be
the same 15 machines as specified in the static quota example.

High-energy physics users indicate which group their jobs should go in
with the submit description file identification:

.. code-block:: condor-submit

    accounting_group = group_physics.hep
    accounting_group_user = higgs

In all these examples so far, the hierarchy is merely a notational
convenience. Each of the examples could be implemented with a flat
structure, although it might be more confusing for the administrator.
Surplus is the concept that creates a true hierarchy.

If a given group or sub-group accepts surplus, then that given group is
allowed to exceed its configured quota, by using the leftover, unused
quota of other groups. Surplus is disabled for all groups by default.
Accepting surplus may be enabled for all groups by setting
:macro:`GROUP_ACCEPT_SURPLUS` to
``True``. Surplus may be enabled for individual groups by setting
:macro:`GROUP_ACCEPT_SURPLUS_<groupname>` to ``True``. Consider
the following example:

.. code-block:: condor-config

      GROUP_NAMES = group_physics, group_physics.hep, group_physics.lep, group_chemistry
      GROUP_QUOTA_group_physics     =   20
      GROUP_QUOTA_group_physics.hep =   15
      GROUP_QUOTA_group_physics.lep =    5
      GROUP_QUOTA_group_chemistry   =   10
      GROUP_ACCEPT_SURPLUS = false
      GROUP_ACCEPT_SURPLUS_group_physics = false
      GROUP_ACCEPT_SURPLUS_group_physics.lep = true
      GROUP_ACCEPT_SURPLUS_group_physics.hep = true

This configuration is the same as above for the chemistry users.
However, :macro:`GROUP_ACCEPT_SURPLUS` is set to ``False`` globally,
``False`` for the physics parent group, and ``True`` for the subgroups
group_physics.lep and group_physics.hep. This means that
group_physics.lep and group_physics.hep are allowed to exceed their
quota of 15 and 5, but their sum cannot exceed 20, for that is their
parent's quota. If the group_physics had :macro:`GROUP_ACCEPT_SURPLUS` set
to ``True``, then either group_physics.lep and group_physics.hep would
not be limited by quota.

Surplus slots are distributed bottom-up from within the quota tree. That
is, any leaf nodes of this tree with excess quota will share it with any
peers which accept surplus. Any subsequent excess will then be passed up
to the parent node and over to all of its children, recursively. Any
node that does not accept surplus implements a hard cap on the number of
slots that the sum of its children use.

After the *condor_negotiator* calculates the quota assigned to each group,
possibly adding in surplus, it then negotiates with the *condor_schedd* daemons
in the system to try to match jobs to each group. It does this one group at a
time. By default, it goes in "starvation group order." That is, the group whose
current usage is the smallest fraction of its quota goes first, then the next,
and so on. The "<none>" group implicitly at the root of the tree goes last.
This ordering can be replaced by defining configuration variable
:macro:`GROUP_SORT_EXPR`. The *condor_negotiator* evaluates this ClassAd
expression for each group ClassAd, sorts the groups by the floating point
result, and then negotiates with the smallest positive value going first.
Available attributes for sorting with :macro:`GROUP_SORT_EXPR` include:

+-------------------------+------------------------------------------+
| Attribute Name          | Description                              |
+=========================+==========================================+
| AccountingGroup         | A string containing the group name       |
+-------------------------+------------------------------------------+
| GroupQuota              | The computed limit for this group        |
+-------------------------+------------------------------------------+
| GroupResourcesInUse     | The total slot weight used by this group |
+-------------------------+------------------------------------------+
| GroupResourcesAllocated | Quota allocated this cycle               |
+-------------------------+------------------------------------------+

Table 3.3: Attributes visible to GROUP_SORT_EXPR


One possible group quota policy is strict priority. For example, a site
prefers physics users to match as many slots as they can, and only when
all the physics jobs are running, and idle slots remain, are chemistry
jobs allowed to run. The default "starvation group order" can be used to
implement this. By setting configuration variable
:macro:`NEGOTIATOR_ALLOW_QUOTA_OVERSUBSCRIPTION` to ``True``, and
setting the physics quota to a number so large that it cannot ever be
met, such as one million, the physics group will always be the "most
starving" group, will always negotiate first, and will always be unable
to meet the quota. Only when all the physics jobs are running will the
chemistry jobs then run. If the chemistry quota is set to a value
smaller than physics, but still larger than the pool, this policy can
support a third, even lower priority group, and so on.

The :tool:`condor_userprio` command can show the current quotas in effect,
and the current usage by group. For example:

.. code-block:: console

    $ condor_userprio -quotas
    Last Priority Update: 11/12 15:18
    Group                    Effective  Config     Use    Subtree  Requested
    Name                       Quota     Quota   Surplus   Quota   Resources
    ------------------------ --------- --------- ------- --------- ----------
    group_physics.hep            15.00     15.00 no          15.00         60
    group_physics.lep             5.00      5.00 no           5.00         60
    ------------------------ --------- --------- ------- --------- ----------
    Number of users: 2                                 ByQuota

This shows that there are two groups, each with 60 jobs in the queue.
group_physics.hep has a quota of 15 cores, and group_physics.lep
has 5 cores. Other options to :tool:`condor_userprio`, such as **-most**
will also show the number of cores in use.

Setting Accounting Group automatically per user
'''''''''''''''''''''''''''''''''''''''''''''''

:index:`group quotas`
:index:`accounting groups`

By default, any user can put the jobs into any accounting group by
setting parameters in the submit file.  This can be useful if a person
is a member of multiple groups.  However, many sites want to force all
jobs submitted by a given user into one accounting group, and forbid
the user to submit to any other group.  An HTCondor metaknob makes this
easy.  By adding to the access point's configuration, the setting

.. code-block:: condor-config

     USE Feature: AssignAccountingGroup(file_name_of_map)


The admin can create a file that maps the users into their required
accounting groups, and makes the attributes immutable, so they can't
be changed.  The format of this map file is like other classad map
files:  Lines of three columns.  The first should be an asterisk 
``*``.  The second column is the name of the user, and the final is the
accounting group that user should always submit to.  For example,

.. code-block:: text

    * Alice	group_physics
    * Bob	group_atlas
    * Carol group_physics
    * /^student_.*/	group_students

The second field can be a regular expression, if
enclosed in ``//``.  Note that this is on the submit side, and the
administrator will still need to create these group names and give them
a quota on the central manager machine.  This file is re-read on a
:tool:`condor_reconfig`.  The third field can also be a comma-separated list.
If so, it represents the set of valid accounting groups a user can
opt into.  If the user does not set an accounting group in the submit file
the first entry in the list will be used.

Concurrency Limits
''''''''''''''''''

:index:`concurrency limits`

Concurrency limits allow an administrator to limit the number of
concurrently running jobs that declare that they use some pool-wide
resource. This limit is applied globally to all jobs submitted from all
schedulers across one HTCondor pool; the limits are not applied to
scheduler, local, or grid universe jobs. This is useful in the case of a
shared resource, such as an NFS or database server that some jobs use,
where the administrator needs to limit the number of jobs accessing the
server.

The administrator must predefine the names and capacities of the
resources to be limited in the negotiator's configuration file. The job
submitter must declare in the submit description file which resources
the job consumes.

The administrator chooses a name for the limit. Concurrency limit names
are case-insensitive. The names are formed from the alphabet letters 'A'
to 'Z' and 'a' to 'z', the numerical digits 0 to 9, the underscore
character '_' , and at most one period character. The names cannot
start with a numerical digit.

For example, assume that there are 3 licenses for the X software, so
HTCondor should constrain the number of running jobs which need the X
software to 3. The administrator picks XSW as the name of the resource
and sets the configuration

.. code-block:: condor-config

    XSW_LIMIT = 3

where ``XSW`` is the invented name of this resource, and this name is
appended with the string ``_LIMIT``. With this limit, a maximum of 3
jobs declaring that they need this resource may be executed
concurrently.

In addition to named limits, such as in the example named limit ``XSW``,
configuration may specify a concurrency limit for all resources that are
not covered by specifically-named limits. The configuration variable
:macro:`CONCURRENCY_LIMIT_DEFAULT` sets this value. For example,

.. code-block:: condor-config

    CONCURRENCY_LIMIT_DEFAULT = 1

will enforce a limit of at most 1 running job that declares a usage of
an unnamed resource. If :macro:`CONCURRENCY_LIMIT_DEFAULT` is omitted from
the configuration, then no limits are placed on the number of
concurrently executing jobs for which there is no specifically-named
concurrency limit.

The job must declare its need for a resource by placing a command in its
submit description file or adding an attribute to the job ClassAd. In
the submit description file, an example job that requires the X software
adds:

.. code-block:: condor-submit

    concurrency_limits = XSW

This results in the job ClassAd attribute

.. code-block:: condor-classad

    ConcurrencyLimits = "XSW"

Jobs may declare that they need more than one type of resource. In this
case, specify a comma-separated list of resources:

.. code-block:: condor-submit

    concurrency_limits = XSW, DATABASE, FILESERVER

The units of these limits are arbitrary. This job consumes one unit of
each resource. Jobs can declare that they use more than one unit with
syntax that follows the resource name by a colon character and the
integer number of resources. For example, if the above job uses three
units of the file server resource, it is declared with

.. code-block:: condor-submit

    concurrency_limits = XSW, DATABASE, FILESERVER:3

If there are sets of resources which have the same capacity for each
member of the set, the configuration may become tedious, as it defines
each member of the set individually. A shortcut defines a name for a
set. For example, define the sets called ``LARGE`` and ``SMALL``:

.. code-block:: condor-config

    CONCURRENCY_LIMIT_DEFAULT = 5
    CONCURRENCY_LIMIT_DEFAULT_LARGE = 100
    CONCURRENCY_LIMIT_DEFAULT_SMALL = 25

To use the set name in a concurrency limit, the syntax follows the set
name with a period and then the set member's name. Continuing this
example, there may be a concurrency limit named ``LARGE.SWLICENSE``,
which gets the capacity of the default defined for the ``LARGE`` set,
which is 100. A concurrency limit named ``LARGE.DBSESSION`` will also
have a limit of 100. A concurrency limit named ``OTHER.LICENSE`` will
receive the default limit of 5, as there is no set named ``OTHER``.

A concurrency limit may be evaluated against the attributes of a matched
machine. This allows a job to vary what concurrency limits it requires
based on the machine to which it is matched. To implement this, the job
uses submit command :subcom:`concurrency_limits_expr[definition]`
instead of :subcom:`concurrency_limits[definition]`
Consider an example in which execute machines are located on one of two
local networks. The administrator sets a concurrency limit to limit the
number of network intensive jobs on each network to 10. Configuration of
each execute machine advertises which local network it is on. A machine
on ``"NETWORK_A"`` configures

.. code-block:: condor-config

    NETWORK = "NETWORK_A"
    STARTD_ATTRS = $(STARTD_ATTRS) NETWORK

and a machine on ``"NETWORK_B"`` configures

.. code-block:: condor-config

    NETWORK = "NETWORK_B"
    STARTD_ATTRS = $(STARTD_ATTRS) NETWORK

The configuration for the negotiator sets the concurrency limits:

.. code-block:: condor-config

    NETWORK_A_LIMIT = 10
    NETWORK_B_LIMIT = 10

Each network intensive job identifies itself by specifying the limit
within the submit description file:

.. code-block:: condor-submit

    concurrency_limits_expr = TARGET.NETWORK

The concurrency limit is applied based on the network of the matched
machine.

An extension of this example applies two concurrency limits. One limit
is the same as in the example, such that it is based on an attribute of
the matched machine. The other limit is of a specialized application
called ``"SWX"`` in this example. The negotiator configuration is
extended to also include

.. code-block:: condor-config

    SWX_LIMIT = 15

The network intensive job that also uses two units of the ``SWX``
application identifies the needed resources in the single submit
command:

.. code-block:: condor-submit

    concurrency_limits_expr = strcat("SWX:2 ", TARGET.NETWORK)

Submit command :subcom:`concurrency_limits_expr` may not be used together
with submit command :subcom:`concurrency_limits`.

Note that it is possible, under unusual circumstances, for more jobs to
be started than should be allowed by the concurrency limits feature. In
the presence of preemption and dropped updates from the *condor_startd*
daemon to the *condor_collector* daemon, it is possible for the limit
to be exceeded. If the limits are exceeded, HTCondor will not kill any
job to reduce the number of running jobs to meet the limit.

Running Multiple Negotiators in One Pool
''''''''''''''''''''''''''''''''''''''''

Usually, a single HTCondor pool will have a single *condor_collector* instance
running and a single *condor_negotiator* instance running.  However, there are
special situation where you may want to run more than one *condor_negotiator*
against a *condor_collector*, and still consider it one pool.

In such a scenario, each *condor_negotiator* is responsible for some
non-overlapping partition of the slots in the pool.  This might be for
performance -- if you have more than 100,000 slots in the pool, you may need to
shard this pool into several smaller sections in order to lower the time each
negotiator spends.  Because accounting is done at the negotiator level, you
may want to do this to have separate accounting and distinct fair share between
different kinds of machines in your pool.  For example, let's say you have some
GPU machines and non-GPU machines, and you want usage of the non-GPU machine to
not "count" against the fair-share usage of GPU machines.  One way to do this
would be to have a separate negotiator for the GPU machines vs the non-GPU
machines.   At UW-Madison, we have a separate, small subset of our pool for
quick-starting interactive jobs.  By allocating a negotiator to only negotiate
for these few machines, we can speed up the time to match these machines to
interactive users who submit with *condor_submit -i*.

Sharding the negotiator is straightforward.  Simply add the NEGOTIATOR entry to
the :macro:`DAEMON_LIST` on an additional machine.  While it is possible to run
multiple negotiators on one machine, we may not want to, if we are trying to
improve performance.  Then, in each negotiator, set
:macro:`NEGOTIATOR_SLOT_CONSTRAINT` to only match those slots this negotiator
should use.

Running with multiple negotiators also means you need to be careful with the
:tool:`condor_userprio` command.  As there is no default negotiator, you should
always name the specific negotiator you want :tool:`condor_userprio` to talk to
with the `-name` option.

Defragmenting Dynamic Slots
---------------------------

:index:`condor_defrag daemon`

When partitionable slots are used, some attention must be given to the
problem of the starvation of large jobs due to the fragmentation of
slot. The problem is that over time the machine resources may
become partitioned into slots suitable only for running small jobs. If a
sufficient number of these slots do not happen to become idle at the
same time on a machine, then a large job will not be able to claim that
machine, even if the large job has a better priority than the small
jobs.

One way of addressing the partitionable slot fragmentation problem is to
periodically drain all jobs from fragmented machines so that they become
defragmented. The *condor_defrag* daemon implements a configurable
policy for doing that. Its implementation is targeted at machines
configured to run whole-machine jobs and at machines that only have
partitionable slots. The draining of a machine configured to have both
partitionable slots and static slots would have a negative impact on
single slot jobs running in static slots.

To use this daemon, ``DEFRAG`` must be added to :macro:`DAEMON_LIST`, and the
defragmentation policy must be configured. Typically, only one instance
of the *condor_defrag* daemon would be run per pool. It is a
lightweight daemon that should not require a lot of system resources.

Here is an example configuration that puts the *condor_defrag* daemon
to work:

.. code-block:: condor-config

    DAEMON_LIST = $(DAEMON_LIST) DEFRAG
    DEFRAG_INTERVAL = 3600
    DEFRAG_DRAINING_MACHINES_PER_HOUR = 1.0
    DEFRAG_MAX_WHOLE_MACHINES = 20
    DEFRAG_MAX_CONCURRENT_DRAINING = 10

This example policy tells *condor_defrag* to initiate draining jobs
from 1 machine per hour, but to avoid initiating new draining if there
are 20 completely defragmented machines or 10 machines in a draining
state. A full description of each configuration variable used by the
*condor_defrag* daemon may be found in the
:ref:`defrag_config_options` section.

By default, when a machine is drained, existing jobs are gracefully
evicted. This means that each job will be allowed to use the remaining
time promised to it by ``MaxJobRetirementTime``. If the job has not
finished when the retirement time runs out, the job will be killed with
a soft kill signal, so that it has an opportunity to save a checkpoint
(if the job supports this).

By default, no new jobs will be allowed to start while the machine is
draining. To reduce unused time on the machine caused by some jobs
having longer retirement time than others, the eviction of jobs with
shorter retirement time is delayed until the job with the longest
retirement time needs to be evicted.

There is a trade off between reduced starvation and throughput. Frequent
draining of machines reduces the chance of starvation of large jobs.
However, frequent draining reduces total throughput. Some of the
machine's resources may go unused during draining, if some jobs finish
before others. If jobs that cannot produce checkpoints are killed
because they run past the end of their retirement time during draining,
this also adds to the cost of draining.

To reduce these costs, you may set the configuration macro
:macro:`DEFRAG_DRAINING_START_EXPR`. If draining gracefully, the
defrag daemon will set the :macro:`START` expression for
the machine to this value expression. Do not set this to your usual
:macro:`START` expression; jobs accepted while draining will not be given
their ``MaxRetirementTime``. Instead, when the last retiring job
finishes (either terminates or runs out of retirement time), all other
jobs on machine will be evicted with a retirement time of 0. (Those jobs
will be given their ``MaxVacateTime``, as usual.) The machine's
:macro:`START` expression will become ``FALSE`` and stay that way until - as
usual - the machine exits the draining state.

We recommend that you allow only interruptible jobs to start on draining
machines. Different pools may have different ways of denoting
interruptible, but a ``MaxJobRetirementTime`` of 0 is probably a good
sign. You may also want to restrict the interruptible jobs'
``MaxVacateTime`` to ensure that the machine will complete draining
quickly.

To help gauge the costs of draining, the *condor_startd* advertises the
accumulated time that was unused due to draining and the time spent by
jobs that were killed due to draining. These are advertised respectively
in the attributes :ad-attr:`TotalMachineDrainingUnclaimedTime` and
:ad-attr:`TotalMachineDrainingBadput`. The *condor_defrag* daemon averages
these values across the pool and advertises the result in its daemon
ClassAd in the attributes :ad-attr:`AvgDrainingBadput` and
``AvgDrainingUnclaimed``. Details of all attributes published by the
*condor_defrag* daemon are described in the :doc:`/classad-attributes/defrag-classad-attributes` section.

The following command may be used to view the *condor_defrag* daemon
ClassAd:

.. code-block:: console

    $ condor_status -l -any -constraint 'MyType == "Defrag"'

:index:`configuration<single: configuration; SMP machines>`
:index:`configuration<single: configuration; multi-core machines>`

Configuring The HTCondorView Server
-----------------------------------

:index:`Server<single: Server; HTCondorView>`

The HTCondorView server is an alternate use of the *condor_collector*
that logs information on disk, providing a persistent, historical
database of pool state. This includes machine state, as well as the
state of jobs submitted by users.

An existing *condor_collector* may act as the HTCondorView collector
through configuration. This is the simplest situation, because the only
change needed is to turn on the logging of historical information. The
alternative of configuring a new *condor_collector* to act as the
HTCondorView collector is slightly more complicated, while it offers the
advantage that the same HTCondorView collector may be used for several
pools as desired, to aggregate information into one place.

The following sections describe how to configure a machine to run a
HTCondorView server and to configure a pool to send updates to it.

Configuring a Machine to be a HTCondorView Server
'''''''''''''''''''''''''''''''''''''''''''''''''

:index:`configuration<single: configuration; HTCondorView>`

To configure the HTCondorView collector, a few configuration variables
are added or modified for the *condor_collector* chosen to act as the
HTCondorView collector. These configuration variables are described in
:ref:`collector_config_options`. Here are brief explanations of the entries that must be customized:

:macro:`POOL_HISTORY_DIR`
    The directory where historical data will be stored. This directory
    must be writable by whatever user the HTCondorView collector is
    running as (usually the user condor). There is a configurable limit
    to the maximum space required for all the files created by the
    HTCondorView server called (:macro:`POOL_HISTORY_MAX_STORAGE`).

    NOTE: This directory should be separate and different from the
    ``spool`` or ``log`` directories already set up for HTCondor. There
    are a few problems putting these files into either of those
    directories.

:macro:`KEEP_POOL_HISTORY`
    A boolean value that determines if the HTCondorView collector should
    store the historical information. It is ``False`` by default, and
    must be specified as ``True`` in the local configuration file to
    enable data collection.

Once these settings are in place in the configuration file for the
HTCondorView server host, create the directory specified in
:macro:`POOL_HISTORY_DIR` and make it writable by the user the HTCondorView
collector is running as. This is the same user that owns the
``CollectorLog`` file in the ``log`` directory. The user is usually
condor.

If using the existing *condor_collector* as the HTCondorView collector,
no further configuration is needed. To run a different
*condor_collector* to act as the HTCondorView collector, configure
HTCondor to automatically start it.

If using a separate host for the HTCondorView collector, to start it, add the
value ``COLLECTOR`` to :macro:`DAEMON_LIST`, and restart HTCondor on that
host. To run the HTCondorView collector on the same host as another
*condor_collector*, ensure that the two *condor_collector* daemons use
different network ports. Here is an example configuration in which the main
*condor_collector* and the HTCondorView collector are started up by the same
:tool:`condor_master` daemon on the same machine. In this example, the HTCondorView
collector uses port 12345.

.. code-block:: condor-config

      VIEW_SERVER = $(COLLECTOR)
      VIEW_SERVER_ARGS = -f -p 12345
      VIEW_SERVER_ENVIRONMENT = "_CONDOR_COLLECTOR_LOG=$(LOG)/ViewServerLog"
      DAEMON_LIST = MASTER, NEGOTIATOR, COLLECTOR, VIEW_SERVER

For this change to take effect, restart the :tool:`condor_master` on this
host. This may be accomplished with the :tool:`condor_restart` command, if
the command is run with administrator access to the pool.

.. _Central Manager High Availability:

High Availability of the Central Manager
----------------------------------------

:index:`of central manager<single: of central manager; High Availability>`

Interaction with Flocking
'''''''''''''''''''''''''

The HTCondor high availability mechanisms discussed in this section
currently do not work well in configurations involving flocking. The
individual problems listed below interact to make the situation
worse. Because of these problems, we advise against the use of flocking
to pools with high availability mechanisms enabled.

-  The *condor_schedd* has a hard configured list of
   *condor_collector* and *condor_negotiator* daemons, and does not
   query redundant collectors to get the current *condor_negotiator*,
   as it does when communicating with its local pool. As a result, if
   the default *condor_negotiator* fails, the *condor_schedd* does not
   learn of the failure, and thus, talk to the new *condor_negotiator*.
-  When the *condor_negotiator* is unable to communicate with a
   *condor_collector*, it utilizes the next *condor_collector* within
   the list. Unfortunately, it does not start over at the top of the
   list. When combined with the previous problem, a backup
   *condor_negotiator* will never get jobs from a flocked
   *condor_schedd*.

Introduction to High availability
'''''''''''''''''''''''''''''''''

The *condor_negotiator* and *condor_collector* daemons are the heart
of the HTCondor matchmaking system. The availability of these daemons is
critical to an HTCondor pool's functionality. Both daemons usually run
on the same machine, most often known as the central manager. The
failure of a central manager machine prevents HTCondor from matching new
jobs and allocating new slots. High availability of the
*condor_negotiator* and *condor_collector* daemons eliminates this
problem.

Configuration allows one of multiple machines within the pool to
function as the central manager. While there may be many active
*condor_collector* daemons, only a single, active *condor_negotiator*
daemon will be running. The machine with the *condor_negotiator* daemon
running is the active central manager. The other potential central
managers each have a *condor_collector* daemon running; these are the
idle central managers.

All submit and execute machines are configured to report to all
potential central manager machines. :index:`condor_had daemon`

Each potential central manager machine runs the high availability
daemon, *condor_had*. These daemons communicate with each other,
constantly monitoring the pool to ensure that one active central manager
is available. If the active central manager machine crashes or is shut
down, these daemons detect the failure, and they agree on which of the
idle central managers is to become the active one. A protocol determines
this.

In the case of a network partition, idle *condor_had* daemons within
each partition detect (by the lack of communication) a partitioning, and
then use the protocol to chose an active central manager. As long as the
partition remains, and there exists an idle central manager within the
partition, there will be one active central manager within each
partition. When the network is repaired, the protocol returns to having
one central manager.

Through configuration, a specific central manager machine may act as the
primary central manager. While this machine is up and running, it
functions as the central manager. After a failure of this primary
central manager, another idle central manager becomes the active one.
When the primary recovers, it again becomes the central manager. This is
a recommended configuration, if one of the central managers is a
reliable machine, which is expected to have very short periods of
instability. An alternative configuration allows the promoted active
central manager (in the case that the central manager fails) to stay
active after the failed central manager machine returns.

This high availability mechanism operates by monitoring communication
between machines. Note that there is a significant difference in
communications between machines when

#. a machine is down
#. a specific daemon (the *condor_had* daemon in this case) is not
   running, yet the machine is functioning

The high availability mechanism distinguishes between these two, and it
operates based only on the first (when a central manager machine is down). A
lack of executing daemons does not cause the protocol to choose or use a
new active central manager.

The central manager machine contains state information, and this
includes information about user priorities. The information is kept in a
single file, and is used by the central manager machine. Should the
primary central manager fail, a pool with high availability enabled
would lose this information (and continue operation, but with
re-initialized priorities). Therefore, the *condor_replication* daemon
exists to replicate this file on all potential central manager machines.
This daemon promulgates the file in a way that is safe from error, and
more secure than dependence on a shared file system copy.
:index:`condor_replication daemon`
:index:`condor_transferer daemon`

The *condor_replication* daemon runs on each potential central manager
machine as well as on the active central manager machine. There is a
unidirectional communication between the *condor_had* daemon and the
*condor_replication* daemon on each machine. To properly do its job,
the *condor_replication* daemon must transfer state files. When it
needs to transfer a file, the *condor_replication* daemons at both the
sending and receiving ends of the transfer invoke the
*condor_transferer* daemon. These short lived daemons do the task of
file transfer and then exit. Do not place :macro:`TRANSFERER` into
:macro:`DAEMON_LIST`, as it is not a daemon that the :tool:`condor_master` should
invoke or watch over.

Configuration
'''''''''''''

The high availability of central manager machines is enabled through
configuration. It is disabled by default. All machines in a pool must be
configured appropriately in order to make the high availability
mechanism work. See the :ref:`high_availability_config_options` section, for definitions
of these configuration variables.

The *condor_had* and *condor_replication* daemons use the
*condor_shared_port* daemon by default. If you want to use more than
one *condor_had* or *condor_replication* daemon with the
*condor_shared_port* daemon under the same master, you must configure
those additional daemons to use nondefault socket names. (Set the
``-sock`` option in ``<NAME>_ARGS``.) Because the *condor_had* daemon
must know the *condor_replication* daemon's address a priori, you will
also need to set ``<NAME>.REPLICATION_SOCKET_NAME`` appropriately.

The stabilization period is the time it takes for the *condor_had*
daemons to detect a change in the pool state such as an active central
manager failure or network partition, and recover from this change. It
may be computed using the following formula:

.. code-block:: text

    Stabilization Period = 12 * (number of central managers) * $(HAD_CONNECTION_TIMEOUT)

To disable the high availability of central managers mechanism, it is
sufficient to remove :macro:`HAD`, :macro:`REPLICATION`, and :macro:`NEGOTIATOR` from
the :macro:`DAEMON_LIST` configuration variable on all machines, leaving only
one *condor_negotiator* in the pool.

To shut down a currently operating high availability mechanism, follow
the given steps. All commands must be invoked from a host which has
administrative permissions on all central managers. The first three
commands kill all *condor_had*, *condor_replication*, and all running
*condor_negotiator* daemons. The last command is invoked on the host
where the single *condor_negotiator* daemon is to run.

.. code-block:: console

    # condor_off -all -neg
    # condor_off -all -subsystem -replication
    # condor_off -all -subsystem -had
    # condor_on -neg

When configuring *condor_had* to control the *condor_negotiator*, if
the default backoff constant value is too small, it can result in a
churning of the *condor_negotiator*, especially in cases in which the
primary negotiator is unable to run due to misconfiguration. In these
cases, the :tool:`condor_master` will kill the *condor_had* after the
*condor_negotiator* exists, wait a short period, then restart
*condor_had*. The *condor_had* will then win the election, so the
secondary *condor_negotiator* will be killed, and the primary will be
restarted, only to exit again. If this happens too quickly, neither
*condor_negotiator* will run long enough to complete a negotiation
cycle, resulting in no jobs getting started. Increasing this value via
:macro:`MASTER_HAD_BACKOFF_CONSTANT` to be larger than a typical
negotiation cycle can help solve this problem.

To run a high availability pool without the replication feature, do the
following operations:

#. Set the :macro:`HAD_USE_REPLICATION`
   configuration variable to ``False``, and thus disable the replication
   on configuration level.
#. Remove :macro:`REPLICATION` from both :macro:`DAEMON_LIST` and
   :macro:`DC_DAEMON_LIST` in the configuration file.

Sample Configuration
''''''''''''''''''''

:index:`sample configuration<single: sample configuration; High Availability>`

This section provides sample configurations for high availability.

We begin with a sample configuration using shared port, and then include
a sample configuration for not using shared port. Both samples relate to
the high availability of central managers.

Each sample is split into two parts: the configuration for the central
manager machines, and the configuration for the machines that will not
be central managers.

The following shared-port configuration is for the central manager
machines.

.. code-block:: condor-config

    ## THE FOLLOWING MUST BE IDENTICAL ON ALL CENTRAL MANAGERS

    CENTRAL_MANAGER1 = cm1.domain.name
    CENTRAL_MANAGER2 = cm2.domain.name
    CONDOR_HOST = $(CENTRAL_MANAGER1), $(CENTRAL_MANAGER2)

    # Since we're using shared port, we set the port number to the shared
    # port daemon's port number.  NOTE: this assumes that each machine in
    # the list is using the same port number for shared port.  While this
    # will be true by default, if you've changed it in configuration any-
    # where, you need to reflect that change here.

    HAD_USE_SHARED_PORT = TRUE
    HAD_LIST = \
    $(CENTRAL_MANAGER1):$(SHARED_PORT_PORT), \
    $(CENTRAL_MANAGER2):$(SHARED_PORT_PORT)

    REPLICATION_USE_SHARED_PORT = TRUE
    REPLICATION_LIST = \
    $(CENTRAL_MANAGER1):$(SHARED_PORT_PORT), \
    $(CENTRAL_MANAGER2):$(SHARED_PORT_PORT)

    # The recommended setting.
    HAD_USE_PRIMARY = TRUE

    # If you change which daemon(s) you're making highly-available, you must
    # change both of these values.
    HAD_CONTROLLEE = NEGOTIATOR
    MASTER_NEGOTIATOR_CONTROLLER = HAD

    ## THE FOLLOWING MAY DIFFER BETWEEN CENTRAL MANAGERS

    # The daemon list may contain additional entries.
    DAEMON_LIST = MASTER, COLLECTOR, NEGOTIATOR, HAD, REPLICATION

    # Using replication is optional.
    HAD_USE_REPLICATION = TRUE

    # This is the default location for the state file.
    STATE_FILE = $(SPOOL)/Accountantnew.log

    # See note above the length of the negotiation cycle.
    MASTER_HAD_BACKOFF_CONSTANT = 360

The following shared-port configuration is for the machines that will not be central managers.

.. code-block:: condor-config

    CENTRAL_MANAGER1 = cm1.domain.name
    CENTRAL_MANAGER2 = cm2.domain.name
    CONDOR_HOST = $(CENTRAL_MANAGER1), $(CENTRAL_MANAGER2)

The following configuration sets fixed port numbers for the central
manager machines.

.. code-block:: condor-config

    ##########################################################################
    # A sample configuration file for central managers, to enable the        #
    # the high availability  mechanism.                                      #
    ##########################################################################

    #########################################################################
    ## THE FOLLOWING MUST BE IDENTICAL ON ALL POTENTIAL CENTRAL MANAGERS.   #
    #########################################################################
    ## For simplicity in writing other expressions, define a variable
    ## for each potential central manager in the pool.
    ## These are samples.
    CENTRAL_MANAGER1 = cm1.domain.name
    CENTRAL_MANAGER2 = cm2.domain.name
    ## A list of all potential central managers in the pool.
    CONDOR_HOST = $(CENTRAL_MANAGER1),$(CENTRAL_MANAGER2)

    ## Define the port number on which the condor_had daemon will
    ## listen.  The port must match the port number used
    ## for when defining HAD_LIST.  This port number is
    ## arbitrary; make sure that there is no port number collision
    ## with other applications.
    HAD_PORT = 51450
    HAD_ARGS = -f -p $(HAD_PORT)

    ## The following macro defines the port number condor_replication will listen
    ## on this machine. This port should match the port number specified
    ## for that replication daemon in the REPLICATION_LIST
    ## Port number is arbitrary (make sure no collision with other applications)
    ## This is a sample port number
    REPLICATION_PORT = 41450
    REPLICATION_ARGS = -p $(REPLICATION_PORT)

    ## The following list must contain the same addresses in the same order
    ## as CONDOR_HOST. In addition, for each hostname, it should specify
    ## the port number of condor_had daemon running on that host.
    ## The first machine in the list will be the PRIMARY central manager
    ## machine, in case HAD_USE_PRIMARY is set to true.
    HAD_LIST = \
    $(CENTRAL_MANAGER1):$(HAD_PORT), \
    $(CENTRAL_MANAGER2):$(HAD_PORT)

    ## The following list must contain the same addresses
    ## as HAD_LIST. In addition, for each hostname, it should specify
    ## the port number of condor_replication daemon running on that host.
    ## This parameter is mandatory and has no default value
    REPLICATION_LIST = \
    $(CENTRAL_MANAGER1):$(REPLICATION_PORT), \
    $(CENTRAL_MANAGER2):$(REPLICATION_PORT)

    ## The following is the name of the daemon that the HAD controls.
    ## This must match the name of a daemon in the master's DAEMON_LIST.
    ## The default is NEGOTIATOR, but can be any daemon that the master
    ## controls.
    HAD_CONTROLLEE = NEGOTIATOR

    ## HAD connection time.
    ## Recommended value is 2 if the central managers are on the same subnet.
    ## Recommended value is 5 if Condor security is enabled.
    ## Recommended value is 10 if the network is very slow, or
    ## to reduce the sensitivity of HA daemons to network failures.
    HAD_CONNECTION_TIMEOUT = 2

    ##If true, the first central manager in HAD_LIST is a primary.
    HAD_USE_PRIMARY = true


    ###################################################################
    ## THE PARAMETERS BELOW ARE ALLOWED TO BE DIFFERENT ON EACH       #
    ## CENTRAL MANAGER                                                #
    ## THESE ARE MASTER SPECIFIC PARAMETERS
    ###################################################################


    ## the master should start at least these four daemons
    DAEMON_LIST = MASTER, COLLECTOR, NEGOTIATOR, HAD, REPLICATION


    ## Enables/disables the replication feature of HAD daemon
    ## Default: false
    HAD_USE_REPLICATION = true

    ## Name of the file from the SPOOL directory that will be replicated
    ## Default: $(SPOOL)/Accountantnew.log
    STATE_FILE = $(SPOOL)/Accountantnew.log

    ## Period of time between two successive awakenings of the replication daemon
    ## Default: 300
    REPLICATION_INTERVAL = 300

    ## Period of time, in which transferer daemons have to accomplish the
    ## downloading/uploading process
    ## Default: 300
    MAX_TRANSFER_LIFETIME = 300


    ## Period of time between two successive sends of classads to the collector by HAD
    ## Default: 300
    HAD_UPDATE_INTERVAL = 300


    ## The HAD controls the negotiator, and should have a larger
    ## backoff constant
    MASTER_NEGOTIATOR_CONTROLLER = HAD
    MASTER_HAD_BACKOFF_CONSTANT = 360

The configuration for machines that will not be central managers is
identical for the fixed- and shared- port cases.

.. code-block:: condor-config

    ##########################################################################
    # Sample configuration relating to high availability for machines        #
    # that DO NOT run the condor_had daemon.                                 #
    ##########################################################################

    ## For simplicity define a variable for each potential central manager
    ## in the pool.
    CENTRAL_MANAGER1 = cm1.domain.name
    CENTRAL_MANAGER2 = cm2.domain.name
    ## List of all potential central managers in the pool
    CONDOR_HOST = $(CENTRAL_MANAGER1),$(CENTRAL_MANAGER2)


Monitoring with Ganglia, Elasticsearch, etc.
--------------------------------------------

:index:`monitoring<single: monitoring; pool management>`
:index:`monitoring pools` :index:`pool monitoring`

HTCondor keeps operational data about different aspects of the system in
different places: The *condor_collector* stores current data about all the
slots and all the daemons in the system.  If absent ads are enabled, the
*condor_collector* also stores information about slots that are no longer in
the system, for a fixed amount of time.  All this data may be queried with
appropriate options to the :tool:`condor_status` command. The AP's job history file
stores data about recent completed and removed jobs, similarly, each EP stores
a startd_history file with information about jobs that have only run on that
EP. Both of these may be queried with the :tool:`condor_history` command.

While using :tool:`condor_status` or :tool:`condor_history` works well for one-off or
ad-hoc queries, both tend to be slow, because none of the data is indexed or
stored in a proper database.  Furthermore, all these data sources age old data
out quickly.  Also, there is no graphical UI provided to visualize or analyze
any of the data.

As there are many robust, well-documented systems to do these sorts of things,
the best solution is to copy the original data out of the proprietary HTCondor
formats and into third party monitoring, database and visualization systems.

The *condor_gangliad* is an HTCSS daemon that periodically copies data out of
the *condor_collector* and into the ganglia monitoring system.  It can also be
used to populate grafana.  :tool:`condor_adstash` is a HTCSS daemon which can copy
job history information out of the AP's history file and into the Elasticsearch
database for further querying.

Ganglia
-------

:index:`with Ganglia<single: with Ganglia; Monitoring>`
:index:`Ganglia monitoring`
:index:`condor_gangliad daemon`

Installation and configuration of Ganglia itself is beyond the scope of this
document: complete information is available at the ganglia homepage at
(`http://ganglia.info/ <http://ganglia.info/>`_), from the O'Reilly book on
the subject, or numerous webpages.

Generally speaking, the *condor_gangliad* should be setup to run on the same
system where the ganglia *gmetad* is running.  Unless the pool is exceptionally
large, putting the gmetad and the *condor_gangliad* on the central manager
machine is a good choice.  To enable the *condor_gangliad*, simply add
the line

.. code-block:: condor-config

      use FEATURE: ganglia

to the config file on the central manager machine, and :tool:`condor_restart` the
HTCondor system on that machine.  If the *condor_gangliad* daemon is to run on
a different machine than the one running Ganglia's *gmetad*, modify
configuration variable :macro:`GANGLIA_GSTAT_COMMAND` to get the list of
monitored hosts from the master *gmond* program.

The above steps alone should be sufficient to get a default set of metrics
about the pool into ganglia.  Additional metrics, tuning and other
information, if needed, follows.

By default, the *condor_gangliad* will only propagate metrics to hosts that are
already monitored by Ganglia. Set configuration variable
:macro:`GANGLIA_SEND_DATA_FOR_ALL_HOSTS` to ``True`` to set up a Ganglia host
to monitor a pool not monitored by Ganglia or have a heterogeneous pool where
some hosts are not monitored. In this case, default graphs that Ganglia
provides will not be present. However, the HTCondor metrics will appear.

On large pools, setting configuration variable
:macro:`GANGLIAD_PER_EXECUTE_NODE_METRICS` to ``False`` will reduce the amount
of data sent to Ganglia. The execute node data is the least important to
monitor. One can also limit the amount of data by setting configuration
variable :macro:`GANGLIAD_REQUIREMENTS` Be aware that aggregate sums over the
entire pool will not be accurate if this variable limits the ClassAds queried.

Metrics to be sent to Ganglia are specified in files within the directory
specified by variable :macro:`GANGLIAD_METRICS_CONFIG_DIR`.  Here is an example
of a single metric definition given as a New ClassAd:

.. code-block:: condor-classad

    [
      Name   = "JobsSubmitted";
      Desc   = "Number of jobs submitted";
      Units  = "jobs";
      TargetType = "Scheduler";
    ]

A nice set of default metrics is in file:
``$(GANGLIAD_METRICS_CONFIG_DIR)/00_default_metrics``.

Recognized metric attribute names and their use:

 Name
    The name of this metric, which corresponds to the ClassAd attribute
    name. Metrics published for the same machine must have unique names.
 Value
    A ClassAd expression that produces the value when evaluated. The
    default value is the value in the daemon ClassAd of the attribute
    with the same name as this metric.
 Desc
    A brief description of the metric. This string is displayed when the
    user holds the mouse over the Ganglia graph for the metric.
 Verbosity
    The integer verbosity level of this metric. Metrics with a higher
    verbosity level than that specified by configuration variable
    :macro:`GANGLIAD_VERBOSITY` will not be published.
 TargetType
    A string containing a comma-separated list of daemon ClassAd types
    that this metric monitors. The specified values should match the
    value of :ad-attr:`MyType` of the daemon ClassAd. In addition, there are
    special values that may be included. "Machine_slot1" may be
    specified to monitor the machine ClassAd for slot 1 only. This is
    useful when monitoring machine-wide attributes. The special value
    "ANY" matches any type of ClassAd.
 Requirements
    A boolean expression that may restrict how this metric is
    incorporated. It defaults to ``True``, which places no restrictions
    on the collection of this ClassAd metric.
 Title
    The graph title used for this metric. The default is the metric
    name.
 Group
    A string specifying the name of this metric's group. Metrics are
    arranged by group within a Ganglia web page. The default is
    determined by the daemon type. Metrics in different groups must have
    unique names.
 Cluster
    A string specifying the cluster name for this metric. The default
    cluster name is taken from the configuration variable
    :macro:`GANGLIAD_DEFAULT_CLUSTER`.
 Units
    A string describing the units of this metric.
 Scale
    A scaling factor that is multiplied by the value of the ``Value``
    attribute. The scale factor is used when the value is not in the
    basic unit or a human-interpretable unit. For example, duty cycle is
    commonly expressed as a percent, but the HTCondor value ranges from
    0 to 1. So, duty cycle is scaled by 100. Some metrics are reported
    in KiB. Scaling by 1024 allows Ganglia to pick the appropriate
    units, such as number of bytes rather than number of KiB. When
    scaling by large values, converting to the "float" type is
    recommended.
 Derivative
    A boolean value that specifies if Ganglia should graph the
    derivative of this metric. If ``True``, and this metric is an aggregate metric, then the derivative of
    each value to be aggregated is computed, and the aggregate function is applied to these derivatives.
    This is useful for numeric attributes that are "counters", i.e. they start at zero and are
    monotonically increasing throughout the lifetime of a slot or a daemon.  
    For example, consider the schedd ad attribute :ad-attr:`FileTransferUploadBytes` which publishes the total number of
    bytes uploaded by the schedd. If ``Derivative`` is set to ``True``, and this metric is an aggregate metric, then the sum of the derivatives of "FileTransferUploadBytes"
    across the pool is computed and published. The derivative of "FileTransferUploadBytes" for each ClassAd is the positive change
    in the value of "FileTransferUploadBytes" since the last update.
    If this metric is not an aggregate metric, then computing of the derivative is performed by
    Ganglia as usual (note: this requires Ganglia versions 3.4 or later).
 Type
    A string specifying the type of the metric. Possible values are
    "double", "float", "int32", "uint32", "int16", "uint16", "int8",
    "uint8", and "string". The default is "string" for string values,
    the default is "int32" for integer values, the default is "float"
    for real values, and the default is "int8" for boolean values.
    Integer values can be coerced to "float" or "double". This is
    especially important for values stored internally as 64-bit values.
 Regex
    This string value specifies a regular expression that matches
    attributes to be monitored by this metric. This is useful for
    dynamic attributes that cannot be enumerated in advance, because
    their names depend on dynamic information such as the users who are
    currently running jobs. When this is specified, one metric per
    matching attribute is created. The default metric name is the name
    of the matched attribute, and the default value is the value of that
    attribute. As usual, the ``Value`` expression may be used when the
    raw attribute value needs to be manipulated before publication.
    However, since the name of the attribute is not known in advance, a
    special ClassAd attribute in the daemon ClassAd is provided to allow
    the ``Value`` expression to refer to it. This special attribute is
    named ``Regex``. Another special feature is the ability to refer to
    text matched by regular expression groups defined by parentheses
    within the regular expression. These may be substituted into the
    values of other string attributes such as ``Name`` and ``Desc``.
    This is done by putting macros in the string values. "\\\\1" is
    replaced by the first group, "\\\\2" by the second group, and so on.
 Aggregate
    This string value specifies an aggregation function to apply,
    instead of publishing individual metrics for each daemon ClassAd.
    Possible values are "sum", "avg", "max", and "min".
 AggregateGroup
    When an aggregate function has been specified, this string value
    specifies which aggregation group the current daemon ClassAd belongs
    to. The default is the metric ``Name``. This feature works like
    GROUP BY in SQL. The aggregation function produces one result per
    value of ``AggregateGroup``. A single aggregate group would
    therefore be appropriate for a pool-wide metric. As an example, to
    publish the sum of an attribute across different types of slot
    ClassAds, make the metric name an expression that is unique to each
    type. The default ``AggregateGroup`` would be set accordingly. Note
    that the assumption is still that the result is a pool-wide metric,
    so by default it is associated with the *condor_collector* daemon's
    host. To group by machine and publish the result into the Ganglia
    page associated with each machine, make the ``AggregateGroup``
    contain the machine name and override the default ``Machine``
    attribute to be the daemon's machine name, rather than the
    *condor_collector* daemon's machine name.
 Machine
    The name of the host associated with this metric. If configuration
    variable :macro:`GANGLIAD_DEFAULT_MACHINE` is not specified, the
    default is taken from the ``Machine`` attribute of the daemon
    ClassAd. If the daemon name is of the form name@hostname, this may
    indicate that there are multiple instances of HTCondor running on
    the same machine. To avoid the metrics from these instances
    overwriting each other, the default machine name is set to the
    daemon name in this case. For aggregate metrics, the default value
    of ``Machine`` will be the name of the *condor_collector* host.
 IP
    A string containing the IP address of the host associated with this
    metric. If :macro:`GANGLIAD_DEFAULT_IP` is not specified, the default is
    extracted from the ``MyAddress`` attribute of the daemon ClassAd.
    This value must be unique for each machine published to Ganglia. It
    need not be a valid IP address. If the value of ``Machine`` contains
    an "@" sign, the default IP value will be set to the same value as
    ``Machine`` in order to make the IP value unique to each instance of
    HTCondor running on the same host.
 Lifetime
    A positive integer value representing the max number of seconds
    without updating a metric will be kept before deletion. This is
    represented in ganglia as DMAX. If no Lifetime is defined for a
    metric then the default value will be set to a calculated value
    based on the ganglia publish interval with a minimum value set by
    :macro:`GANGLIAD_MIN_METRIC_LIFETIME`.

Absent ClassAds
---------------

:index:`absent ClassAds<single: absent ClassAds; pool management>`
:index:`absent ClassAd` :index:`absent ClassAd<single: absent ClassAd; ClassAd>`

By default, HTCondor assumes that slots are transient: the
*condor_collector* will discard ClassAds older than :macro:`CLASSAD_LIFETIME`
seconds. Its default configuration value is 15 minutes, and as such, the
default value for :macro:`UPDATE_INTERVAL` will pass three times before
HTCondor forgets about a resource. In some pools, especially those with
dedicated resources, this approach may make it unnecessarily difficult to
determine what the composition of the pool ought to be, in the sense of knowing
which machines would be in the pool, if HTCondor were properly functioning on
all of them.

This assumption of transient machines can be modified by the use of absent
ClassAds. When a slot ClassAd would otherwise expire, the *condor_collector*
evaluates the configuration variable :macro:`ABSENT_REQUIREMENTS` against the
machine ClassAd. If ``True``, the machine ClassAd will be saved in a persistent
manner and be marked as absent; this causes the machine to appear in the output
of ``condor_status -absent``. When the machine returns to the pool, its first
update to the *condor_collector* will invalidate the absent machine ClassAd.

Absent ClassAds, like offline ClassAds, are stored to disk to ensure that they
are remembered, even across *condor_collector* crashes. The configuration
variable :macro:`COLLECTOR_PERSISTENT_AD_LOG` defines the file in which the
ClassAds are stored.
Absent ClassAds are retained on disk as maintained by the *condor_collector*
for a length of time in seconds defined by the configuration variable
:macro:`ABSENT_EXPIRE_ADS_AFTER`. A value of 0 for this variable means that the
ClassAds are never discarded, and the default value is thirty days.

Absent ClassAds are only returned by the *condor_collector* and displayed when
the **-absent** option to :tool:`condor_status` is specified, or when the absent
machine ClassAd attribute is mentioned on the :tool:`condor_status` command line.
This renders absent ClassAds invisible to the rest of the HTCondor
infrastructure.

A daemon may inform the *condor_collector* that the daemon's ClassAd should not
expire, but should be removed right away; the daemon asks for its ClassAd to be
invalidated. It may be useful to place an invalidated ClassAd in the absent
state, instead of having it removed as an invalidated ClassAd. An example of a
ClassAd that could benefit from being absent is a system with an
uninterruptible power supply that shuts down cleanly but unexpectedly as a
result of a power outage. To cause all invalidated ClassAds to become absent
instead of invalidated, set :macro:`EXPIRE_INVALIDATED_ADS` to ``True``.
Invalidated ClassAds will instead be treated as if they expired, including when
evaluating :macro:`ABSENT_REQUIREMENTS`.

Elasticsearch
-------------

:index:`Elasticsearch`
:index:`adstash`
:index:`condor_adstash`

HTCondor supports pushing *condor_schedd* and *condor_startd* job
and job epoch ClassAds to Elasticsearch (and other targets) via the
:tool:`condor_adstash` tool/daemon.
:tool:`condor_adstash` collects job ClassAds as specified by its
configuration, either querying specified daemons
or reading job ClassAds from a specified file,
converts each ClassAd to a JSON document,
and pushes each doc to the configured Elasticsearch index.
The index is automatically created if it does not exist, and fields
are added and configured based on well known job ClassAd attributes.
(Custom attributes are also pushed, though always as keyword fields.)

:tool:`condor_adstash` is a Python 3.6+ script that uses the
HTCondor :ref:`apis/python-bindings/index:Python Bindings`
and the
`Python Elasticsearch Client <https://elasticsearch-py.readthedocs.io/>`_,
both of which must be available to the system Python 3 installation
if using the daemonized version of :tool:`condor_adstash`.
:tool:`condor_adstash` can also be run as a stand alone tool (e.g. in a
Python 3 virtual environment containing the necessary libraries).

Running :tool:`condor_adstash` as a daemon (i.e. under the watch of the
:tool:`condor_master`) can be enabled by adding
``use feature : adstash``
to your HTCondor configuration.
By default, this configuration will poll the job history on all
*condor_schedds* that report to the ``$(CONDOR_HOST)`` *condor_collector*
every 20 minutes and push the contents of the job history ClassAds to an
Elasticsearch instance running on ``localhost`` to an index named
``htcondor-000001``.
Your situation and monitoring needs are likely different!
See the ``condor_config.local.adstash`` example configuration file in
the ``examples/`` directory for detailed information on how to modify
your configuration.

If you prefer to run :tool:`condor_adstash` in standalone mode, or are
curious about other ClassAd sources or targets, see the
:doc:`../man-pages/condor_adstash` man page for more
details.

Configuring a Pool to Report to the HTCondorView Server
'''''''''''''''''''''''''''''''''''''''''''''''''''''''

For the HTCondorView server to function, configure the existing
collector to forward ClassAd updates to it. This configuration is only
necessary if the HTCondorView collector is a different collector from
the existing *condor_collector* for the pool. All the HTCondor daemons
in the pool send their ClassAd updates to the regular
*condor_collector*, which in turn will forward them on to the
HTCondorView server.

Define the following configuration variable:

.. code-block:: condor-config

      CONDOR_VIEW_HOST = full.hostname[:portnumber]

where full.hostname is the full host name of the machine running the
HTCondorView collector. The full host name is optionally followed by a
colon and port number. This is only necessary if the HTCondorView
collector is configured to use a port number other than the default.

Place this setting in the configuration file used by the existing
*condor_collector*. It is acceptable to place it in the global
configuration file. The HTCondorView collector will ignore this setting
(as it should) as it notices that it is being asked to forward ClassAds
to itself.

Once the HTCondorView server is running with this change, send a
:tool:`condor_reconfig` command to the main *condor_collector* for the
change to take effect, so it will begin forwarding updates. A query to
the HTCondorView collector will verify that it is working. A query
example:

.. code-block:: console

      $ condor_status -pool condor.view.host[:portnumber]

A *condor_collector* may also be configured to report to multiple
HTCondorView servers. The configuration variable 
:macro:`CONDOR_VIEW_HOST` can be given as a list of HTCondorView
servers separated by commas and/or spaces.

The following demonstrates an example configuration for two HTCondorView
servers, where both HTCondorView servers (and the *condor_collector*)
are running on the same machine, localhost.localdomain:

.. code-block:: condor-config

    VIEWSERV01 = $(COLLECTOR)
    VIEWSERV01_ARGS = -f -p 12345 -local-name VIEWSERV01
    VIEWSERV01_ENVIRONMENT = "_CONDOR_COLLECTOR_LOG=$(LOG)/ViewServerLog01"
    VIEWSERV01.POOL_HISTORY_DIR = $(LOCAL_DIR)/poolhist01
    VIEWSERV01.KEEP_POOL_HISTORY = TRUE
    VIEWSERV01.CONDOR_VIEW_HOST =

    VIEWSERV02 = $(COLLECTOR)
    VIEWSERV02_ARGS = -f -p 24680 -local-name VIEWSERV02
    VIEWSERV02_ENVIRONMENT = "_CONDOR_COLLECTOR_LOG=$(LOG)/ViewServerLog02"
    VIEWSERV02.POOL_HISTORY_DIR = $(LOCAL_DIR)/poolhist02
    VIEWSERV02.KEEP_POOL_HISTORY = TRUE
    VIEWSERV02.CONDOR_VIEW_HOST =

    CONDOR_VIEW_HOST = localhost.localdomain:12345 localhost.localdomain:24680
    DAEMON_LIST = $(DAEMON_LIST) VIEWSERV01 VIEWSERV02

Note that the value of :macro:`CONDOR_VIEW_HOST` for VIEWSERV01 and VIEWSERV02
is unset, to prevent them from inheriting the global value of
:macro:`CONDOR_VIEW_HOST` and attempting to report to themselves or each other. If
the HTCondorView servers are running on different machines where there is no
global value for :macro:`CONDOR_VIEW_HOST`, this precaution is not required.

Scaling and Performance Considerations for the CM
-------------------------------------------------

:index:`performance of the CM`

The central manager (CM), as the central point of an HTCondor system, is
designed to be scalable out of the box to large systems.  As long as it 
is installed on dedicated, reasonable hardware, it should be able to 
manage a local pool of 100,000 cores.  One reason for the scalability is
that the CM is mostly stateless, meaning that it does not keep many records
on disk.  However, as some sites are scaling out beyond this size, there
are some additional considerations and configurations to keep in mind.

Scaling up the Collector
''''''''''''''''''''''''

For pools of the largest size, it may be necessary to set up a 
tree of collectors.  This is described here: 
:ref:`faq/admins/collector-tree:Recipe: Setting up a Collector Tree`


Scaling up the Negotiator
'''''''''''''''''''''''''

The metric to measure the performance of the negotiator is the negotiator
cycle time, which is the time it takes for the negotiator to complete one
complete matchmaking cycle.  In a healthy pool, this may take on the order
of ten minutes.  If the cycle is longer than that, consider sharding the
negotiator as described above.  To the first degree, the cycle time scales
by the product of the number of autoclusters in all the *condor_schedd*
and the number of idle slots.  If you do not need preemption of running
jobs in your system, setting the knob :macro:`NEGOTIATOR_CONSIDER_PREEMPTION`
to false can dramatically speed up the negotiation cycle.
