Setting Up for Special Environments

The following sections describe how to set up HTCondor for use in special environments or configurations.

Configuring HTCondor for Multiple Platforms

A single, initial configuration file may be used for all platforms in an HTCondor pool, with platform-specific settings placed in separate files. This greatly simplifies administration of a heterogeneous pool by allowing specification of platform-independent, global settings in one place, instead of separately for each platform. This is made possible by treating the LOCAL_CONFIG_FILE configuration variable as a list of files, instead of a single file. Of course, this only helps when using a shared file system for the machines in the pool, so that multiple machines can actually share a single set of configuration files.

With multiple platforms, put all platform-independent settings (the vast majority) into the single initial configuration file, which will be shared by all platforms. Then, set the LOCAL_CONFIG_FILE configuration variable from that global configuration file to specify both a platform-specific configuration file and optionally, a local, machine-specific configuration file.

The name of platform-specific configuration files may be specified by using $(ARCH) and $(OPSYS), as defined automatically by HTCondor. For example, for 32-bit Intel Windows 7 machines and 64-bit Intel Linux machines, the files ought to be named:

$ condor_config.INTEL.WINDOWS

Then, assuming these files are in the directory defined by the ETC configuration variable, and machine-specific configuration files are in the same directory, named by each machine’s host name, LOCAL_CONFIG_FILE becomes:

LOCAL_CONFIG_FILE = $(ETC)/condor_config.$(ARCH).$(OPSYS), \

Alternatively, when using AFS, an @sys link may be used to specify the platform-specific configuration file, which lets AFS resolve this link based on platform name. For example, consider a soft link named condor_config.platform that points to condor_config.@sys. In this case, the files might be named:

$ condor_config.i386_linux2
condor_config.platform -> condor_config.@sys

and the LOCAL_CONFIG_FILE configuration variable would be set to

LOCAL_CONFIG_FILE = $(ETC)/condor_config.platform, \

Platform-Specific Configuration File Settings

The configuration variables that are truly platform-specific are:


Full path to the installed HTCondor binaries. While the configuration files may be shared among different platforms, the binaries certainly cannot. Therefore, maintain separate release directories for each platform in the pool.


The full path to the mail program.


Which devices in /dev should be treated as console devices.


Which daemons the condor_master should start up. The reason this setting is platform-specific is to distinguish the condor_kbdd. It is needed on many Linux and Windows machines, and it is not needed on other platforms.

Reasonable defaults for all of these configuration variables will be found in the default configuration files inside a given platform’s binary distribution (except the RELEASE_DIR, since the location of the HTCondor binaries and libraries is installation specific). With multiple platforms, use one of the condor_config files from either running condor_configure or from the $(RELEASE_DIR)/etc/examples/condor_config.generic file, take these settings out, save them into a platform-specific file, and install the resulting platform-independent file as the global configuration file. Then, find the same settings from the configuration files for any other platforms to be set up, and put them in their own platform-specific files. Finally, set the LOCAL_CONFIG_FILE configuration variable to point to the appropriate platform-specific file, as described above.

Not even all of these configuration variables are necessarily going to be different. For example, if an installed mail program understands the -s option in /usr/local/bin/mail on all platforms, the MAIL macro may be set to that in the global configuration file, and not define it anywhere else. For a pool with only Linux or Windows machines, the DAEMON_LIST will be the same for each, so there is no reason not to put that in the global configuration file.

Other Uses for Platform-Specific Configuration Files

An installation may want other configuration variables to be platform-specific. Perhaps a different policy is desired for one of the platforms. Perhaps different people should get the e-mail about problems with the different platforms. There is nothing hard-coded about any of this. What is shared and what should not shared is entirely configurable.

Since the LOCAL_CONFIG_FILE macro can be an arbitrary list of files, an installation can even break up the global, platform-independent settings into separate files. In fact, the global configuration file might only contain a definition for LOCAL_CONFIG_FILE, and all other configuration variables would be placed in separate files.

Different people may be given different permissions to change different HTCondor settings. For example, if a user is to be able to change certain settings, but nothing else, those settings may be placed in a file which was early in the LOCAL_CONFIG_FILE list, to give that user write permission on that file. Then, include all the other files after that one. In this way, if the user was attempting to change settings that the user should not be permitted to change, the settings would be overridden.

This mechanism is quite flexible and powerful. For very specific configuration needs, they can probably be met by using file permissions, the LOCAL_CONFIG_FILE configuration variable, and imagination.

The condor_kbdd

The HTCondor keyboard daemon, condor_kbdd, monitors X events on machines where the operating system does not provide a way of monitoring the idle time of the keyboard or mouse. On Linux platforms, it is needed to detect USB keyboard activity. Otherwise, it is not needed. On Windows platforms, the condor_kbdd is the primary way of monitoring the idle time of both the keyboard and mouse.

The condor_kbdd on Windows Platforms

Windows platforms need to use the condor_kbdd to monitor the idle time of both the keyboard and mouse. By adding KBDD to configuration variable DAEMON_LIST, the condor_master daemon invokes the condor_kbdd, which then does the right thing to monitor activity given the version of Windows running.

With Windows Vista and more recent version of Windows, user sessions are moved out of session 0. Therefore, the condor_startd service is no longer able to listen to keyboard and mouse events. The condor_kbdd will run in an invisible window and should not be noticeable by the user, except for a listing in the task manager. When the user logs out, the program is terminated by Windows. This implementation also appears in versions of Windows that predate Vista, because it adds the capability of monitoring keyboard activity from multiple users.

To achieve the auto-start with user login, the HTCondor installer adds a condor_kbdd entry to the registry key at HKLM\Software\Microsoft\Windows\CurrentVersion\Run. On 64-bit versions of Vista and more recent Windows versions, the entry is actually placed in HKLM\Software\Wow6432Node\Microsoft\Windows\CurrentVersion\Run.

In instances where the condor_kbdd is unable to connect to the condor_startd, it is likely because an exception was not properly added to the Windows firewall.

The condor_kbdd on Linux Platforms

On Linux platforms, great measures have been taken to make the condor_kbdd as robust as possible, but the X window system was not designed to facilitate such a need, and thus is not as efficient on machines where many users frequently log in and out on the console.

In order to work with X authority, which is the system by which X authorizes processes to connect to X servers, the condor_kbdd needs to run with super user privileges. Currently, the condor_kbdd assumes that X uses the HOME environment variable in order to locate a file named .Xauthority. This file contains keys necessary to connect to an X server. The keyboard daemon attempts to set HOME to various users’ home directories in order to gain a connection to the X server and monitor events. This may fail to work if the keyboard daemon is not allowed to attach to the X server, and the state of a machine may be incorrectly set to idle when a user is, in fact, using the machine.

In some environments, the condor_kbdd will not be able to connect to the X server because the user currently logged into the system keeps their authentication token for using the X server in a place that no local user on the current machine can get to. This may be the case for files on AFS, because the user’s .Xauthority file is in an AFS home directory.

There may also be cases where the condor_kbdd may not be run with super user privileges because of political reasons, but it is still desired to be able to monitor X activity. In these cases, change the XDM configuration in order to start up the condor_kbdd with the permissions of the logged in user. If running X11R6.3, the files to edit will probably be in /usr/X11R6/lib/X11/xdm. The .xsession file should start up the condor_kbdd at the end, and the .Xreset file should shut down the condor_kbdd. The -l option can be used to write the daemon’s log file to a place where the user running the daemon has permission to write a file. The file’s recommended location will be similar to $HOME/.kbdd.log, since this is a place where every user can write, and the file will not get in the way. The -pidfile and -k options allow for easy shut down of the condor_kbdd by storing the process ID in a file. It will be necessary to add lines to the XDM configuration similar to

$ condor_kbdd -l $HOME/.kbdd.log -pidfile $HOME/

This will start the condor_kbdd as the user who is currently logged in and write the log to a file in the directory $HOME/.kbdd.log/. This will also save the process ID of the daemon to ˜/, so that when the user logs out, XDM can do:

$ condor_kbdd -k $HOME/

This will shut down the process recorded in file ˜/ and exit.

To see how well the keyboard daemon is working, review the log for the daemon and look for successful connections to the X server. If there are none, the condor_kbdd is unable to connect to the machine’s X server.

Configuring The HTCondorView Server

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

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 condor_collector Configuration File Entries. Here are brief explanations of the entries that must be customized:


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 (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.


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 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 macro: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 condor_master daemon on the same machine. In this example, the HTCondorView collector uses port 12345.

VIEW_SERVER_ARGS = -f -p 12345

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

HTCondor’s Dedicated Scheduling

The dedicated scheduler is a part of the condor_schedd that handles the scheduling of parallel jobs that require more than one machine concurrently running per job. MPI applications are a common use for the dedicated scheduler, but parallel applications which do not require MPI can also be run with the dedicated scheduler. All jobs which use the parallel universe are routed to the dedicated scheduler within the condor_schedd they were submitted to. A default HTCondor installation does not configure a dedicated scheduler; the administrator must designate one or more condor_schedd daemons to perform as dedicated scheduler.

Selecting and Setting Up a Dedicated Scheduler

We recommend that you select a single machine within an HTCondor pool to act as the dedicated scheduler. This becomes the machine from upon which all users submit their parallel universe jobs. The perfect choice for the dedicated scheduler is the single, front-end machine for a dedicated cluster of compute nodes. For the pool without an obvious choice for a access point, choose a machine that all users can log into, as well as one that is likely to be up and running all the time. All of HTCondor’s other resource requirements for a access point apply to this machine, such as having enough disk space in the spool directory to hold jobs. See Directories for more information.

Configuration Examples for Dedicated Resources

Each execute machine may have its own policy for the execution of jobs, as set by configuration. Each machine with aspects of its configuration that are dedicated identifies the dedicated scheduler. And, the ClassAd representing a job to be executed on one or more of these dedicated machines includes an identifying attribute. An example configuration file with the following various policy settings is /etc/examples/condor_config.local.dedicated.resource.

Each execute machine defines the configuration variable DedicatedScheduler, which identifies the dedicated scheduler it is managed by. The local configuration file contains a modified form of

DedicatedScheduler = ""
STARTD_ATTRS = $(STARTD_ATTRS), DedicatedScheduler

Substitute the host name of the dedicated scheduler machine for the string “”.

If running personal HTCondor, the name of the scheduler includes the user name it was started as, so the configuration appears as:

DedicatedScheduler = ""
STARTD_ATTRS = $(STARTD_ATTRS), DedicatedScheduler

All dedicated execute machines must have policy expressions which allow for jobs to always run, but not be preempted. The resource must also be configured to prefer jobs from the dedicated scheduler over all other jobs. Therefore, configuration gives the dedicated scheduler of choice the highest rank. It is worth noting that HTCondor puts no other requirements on a resource for it to be considered dedicated.

Job ClassAds from the dedicated scheduler contain the attribute Scheduler. The attribute is defined by a string of the form

Scheduler = ""

The host name of the dedicated scheduler substitutes for the string

Different resources in the pool may have different dedicated policies by varying the local configuration.

Policy Scenario: Machine Runs Only Jobs That Require Dedicated Resources

One possible scenario for the use of a dedicated resource is to only run jobs that require the dedicated resource. To enact this policy, configure the following expressions:

START     = Scheduler =?= $(DedicatedScheduler)
SUSPEND   = False
PREEMPT   = False
KILL      = False
WANT_VACATE    = False
RANK      = Scheduler =?= $(DedicatedScheduler)

The START expression specifies that a job with the Scheduler attribute must match the string corresponding DedicatedScheduler attribute in the machine ClassAd. The RANK expression specifies that this same job (with the Scheduler attribute) has the highest rank. This prevents other jobs from preempting it based on user priorities. The rest of the expressions disable any other of the condor_startd daemon’s pool-wide policies, such as those for evicting jobs when keyboard and CPU activity is discovered on the machine.

Policy Scenario: Run Both Jobs That Do and Do Not Require Dedicated Resources

While the first example works nicely for jobs requiring dedicated resources, it can lead to poor utilization of the dedicated machines. A more sophisticated strategy allows the machines to run other jobs, when no jobs that require dedicated resources exist. The machine is configured to prefer jobs that require dedicated resources, but not prevent others from running.

To implement this, configure the machine as a dedicated resource as above, modifying only the START expression:

START = True
Policy Scenario: Adding Desktop Resources To The Mix

A third policy example allows all jobs. These desktop machines use a preexisting START expression that takes the machine owner’s usage into account for some jobs. The machine does not preempt jobs that must run on dedicated resources, while it may preempt other jobs as defined by policy. So, the default pool policy is used for starting and stopping jobs, while jobs that require a dedicated resource always start and are not preempted.

The START, SUSPEND, PREEMPT, and macro:RANK policies are set in the global configuration. Locally, the configuration is modified to this hybrid policy by adding a second case.

SUSPEND    = Scheduler =!= $(DedicatedScheduler) && ($(SUSPEND))
PREEMPT    = Scheduler =!= $(DedicatedScheduler) && ($(PREEMPT))
RANK_FACTOR    = 1000000
RANK   = (Scheduler =?= $(DedicatedScheduler) * $(RANK_FACTOR)) \
               + $(RANK)
START  = (Scheduler =?= $(DedicatedScheduler)) || ($(START))

Define RANK_FACTOR to be a larger value than the maximum value possible for the existing rank expression. RANK is a floating point value, so there is no harm in assigning a very large value.

Preemption with Dedicated Jobs

The dedicated scheduler can be configured to preempt running parallel universe jobs in favor of higher priority parallel universe jobs. Note that this is different from preemption in other universes, and parallel universe jobs cannot be preempted either by a machine’s user pressing a key or by other means.

By default, the dedicated scheduler will never preempt running parallel universe jobs. Two configuration variables control preemption of these dedicated resources: SCHEDD_PREEMPTION_REQUIREMENTS and SCHEDD_PREEMPTION_RANK. These variables have no default value, so if either are not defined, preemption will never occur. SCHEDD_PREEMPTION_REQUIREMENTS must evaluate to True for a machine to be a candidate for this kind of preemption. If more machines are candidates for preemption than needed to satisfy a higher priority job, the machines are sorted by SCHEDD_PREEMPTION_RANK, and only the highest ranked machines are taken.

Note that preempting one node of a running parallel universe job requires killing the entire job on all of its nodes. So, when preemption occurs, it may end up freeing more machines than are needed for the new job. Also, preempted jobs will be re-run, starting again from the beginning. Thus, the administrator should be careful when enabling preemption of these dedicated resources. Enable dedicated preemption with the configuration:


In this example, preemption is enabled by user-defined job priority. If a set of machines is running a job at user priority 5, and the user submits a new job at user priority 10, the running job will be preempted for the new job. The old job is put back in the queue, and will begin again from the beginning when assigned to a newly acquired set of machines.

Grouping Dedicated Nodes into Parallel Scheduling Groups

In some parallel environments, machines are divided into groups, and jobs should not cross groups of machines. That is, all the nodes of a parallel job should be allocated to machines within the same group. The most common example is a pool of machine using InfiniBand switches. For example, each switch might connect 16 machines, and a pool might have 160 machines on 10 switches. If the InfiniBand switches are not routed to each other, each job must run on machines connected to the same switch. The dedicated scheduler’s Parallel Scheduling Groups feature supports this operation.

Each condor_startd must define which group it belongs to by setting the ParallelSchedulingGroup variable in the configuration file, and advertising it into the machine ClassAd. The value of this variable is a string, which should be the same for all condor_startd daemons within a given group. The property must be advertised in the condor_startd ClassAd by appending ParallelSchedulingGroup to the STARTD_ATTRS configuration variable.

The submit description file for a parallel universe job which must not cross group boundaries contains

+WantParallelSchedulingGroups = True

The dedicated scheduler enforces the allocation to within a group.

Configuring HTCondor for Running Backfill Jobs

HTCondor can be configured to run backfill jobs whenever the condor_startd has no other work to perform. These jobs are considered the lowest possible priority, but when machines would otherwise be idle, the resources can be put to good use.

Currently, HTCondor only supports using the Berkeley Open Infrastructure for Network Computing (BOINC) to provide the backfill jobs. More information about BOINC is available at

The rest of this section provides an overview of how backfill jobs work in HTCondor, details for configuring the policy for when backfill jobs are started or killed, and details on how to configure HTCondor to spawn the BOINC client to perform the work.

Overview of Backfill jobs in HTCondor

Whenever a resource controlled by HTCondor is in the Unclaimed/Idle state, it is totally idle; neither the interactive user nor an HTCondor job is performing any work. Machines in this state can be configured to enter the Backfill state, which allows the resource to attempt a background computation to keep itself busy until other work arrives (either a user returning to use the machine interactively, or a normal HTCondor job). Once a resource enters the Backfill state, the condor_startd will attempt to spawn another program, called a backfill client, to launch and manage the backfill computation. When other work arrives, the condor_startd will kill the backfill client and clean up any processes it has spawned, freeing the machine resources for the new, higher priority task. More details about the different states an HTCondor resource can enter and all of the possible transitions between them are described in Policy Configuration for Execution Points and for Access Points, especially the condor_startd Policy Configuration and condor_schedd Policy Configuration sections.

At this point, the only backfill system supported by HTCondor is BOINC. The condor_startd has the ability to start and stop the BOINC client program at the appropriate times, but otherwise provides no additional services to configure the BOINC computations themselves. Future versions of HTCondor might provide additional functionality to make it easier to manage BOINC computations from within HTCondor. For now, the BOINC client must be manually installed and configured outside of HTCondor on each backfill-enabled machine.

Defining the Backfill Policy

There are a small set of policy expressions that determine if a condor_startd will attempt to spawn a backfill client at all, and if so, to control the transitions in to and out of the Backfill state. This section briefly lists these expressions. More detail can be found in condor_startd Configuration File Macros.


A boolean value to determine if any backfill functionality should be used. The default value is False.


A string that defines what backfill system to use for spawning and managing backfill computations. Currently, the only supported string is "BOINC".


A boolean expression to control if an HTCondor resource should start a backfill client. This expression is only evaluated when the machine is in the Unclaimed/Idle state and the ENABLE_BACKFILL expression is True.


A boolean expression that is evaluated whenever an HTCondor resource is in the Backfill state. A value of True indicates the machine should immediately kill the currently running backfill client and any other spawned processes, and return to the Owner state.

The following example shows a possible configuration to enable backfill:

# Turn on backfill functionality, and use BOINC

# Spawn a backfill job if we've been Unclaimed for more than 5
# minutes
START_BACKFILL = $(StateTimer) > (5 * $(MINUTE))

# Evict a backfill job if the machine is busy (based on keyboard
# activity or cpu load)
EVICT_BACKFILL = $(MachineBusy)

Overview of the BOINC system

The BOINC system is a distributed computing environment for solving large scale scientific problems. A detailed explanation of this system is beyond the scope of this manual. Thorough documentation about BOINC is available at their website: However, a brief overview is provided here for sites interested in using BOINC with HTCondor to manage backfill jobs.

BOINC grew out of the relatively famous SETI@home computation, where volunteers installed special client software, in the form of a screen saver, that contacted a centralized server to download work units. Each work unit contained a set of radio telescope data and the computation tried to find patterns in the data, a sign of intelligent life elsewhere in the universe, hence the name: “Search for Extra Terrestrial Intelligence at home”. BOINC is developed by the Space Sciences Lab at the University of California, Berkeley, by the same people who created SETI@home. However, instead of being tied to the specific radio telescope application, BOINC is a generic infrastructure by which many different kinds of scientific computations can be solved. The current generation of SETI@home now runs on top of BOINC, along with various physics, biology, climatology, and other applications.

The basic computational model for BOINC and the original SETI@home is the same: volunteers install BOINC client software, called the boinc_client, which runs whenever the machine would otherwise be idle. However, the BOINC installation on any given machine must be configured so that it knows what computations to work for instead of always working on a hard coded computation. The BOINC terminology for a computation is a project. A given BOINC client can be configured to donate all of its cycles to a single project, or to split the cycles between projects so that, on average, the desired percentage of the computational power is allocated to each project. Once the boinc_client starts running, it attempts to contact a centralized server for each project it has been configured to work for. The BOINC software downloads the appropriate platform-specific application binary and some work units from the central server for each project. Whenever the client software completes a given work unit, it once again attempts to connect to that project’s central server to upload the results and download more work.

BOINC participants must register at the centralized server for each project they wish to donate cycles to. The process produces a unique identifier so that the work performed by a given client can be credited to a specific user. BOINC keeps track of the work units completed by each user, so that users providing the most cycles get the highest rankings, and therefore, bragging rights.

Because BOINC already handles the problems of distributing the application binaries for each scientific computation, the work units, and compiling the results, it is a perfect system for managing backfill computations in HTCondor. Many of the applications that run on top of BOINC produce their own application-specific checkpoints, so even if the boinc_client is killed, for example, when an HTCondor job arrives at a machine, or if the interactive user returns, an entire work unit will not necessarily be lost.

Installing the BOINC client software

In HTCondor Version 23.0.12, the boinc_client must be manually downloaded, installed and configured outside of HTCondor. Download the boinc_client executables at

Once the BOINC client software has been downloaded, the boinc_client binary should be placed in a location where the HTCondor daemons can use it. The path will be specified with the HTCondor configuration variable BOINC_Executable.

Additionally, a local directory on each machine should be created where the BOINC system can write files it needs. This directory must not be shared by multiple instances of the BOINC software. This is the same restriction as placed on the spool or execute directories used by HTCondor. The location of this directory is defined by BOINC_InitialDir. The directory must be writable by whatever user the boinc_client will run as. This user is either the same as the user the HTCondor daemons are running as, if HTCondor is not running as root, or a user defined via the BOINC_Owner configuration variable.

Finally, HTCondor administrators wishing to use BOINC for backfill jobs must create accounts at the various BOINC projects they want to donate cycles to. The details of this process vary from project to project. Beware that this step must be done manually, as the boinc_client can not automatically register a user at a given project, unlike the more fancy GUI version of the BOINC client software which many users run as a screen saver. For example, to configure machines to perform work for the Einstein@home project (a physics experiment run by the University of Wisconsin at Milwaukee), HTCondor administrators should go to, fill in the web form, and generate a new Einstein@home identity. This identity takes the form of a project URL (such as followed by an account key, which is a long string of letters and numbers that is used as a unique identifier. This URL and account key will be needed when configuring HTCondor to use BOINC for backfill computations.

Configuring the BOINC client under HTCondor

After the boinc_client has been installed on a given machine, the BOINC projects to join have been selected, and a unique project account key has been created for each project, the HTCondor configuration needs to be modified.

Whenever the condor_startd decides to spawn the boinc_client to perform backfill computations, it will spawn a condor_starter to directly launch and monitor the boinc_client program. This condor_starter is just like the one used to invoke any other HTCondor jobs.

This condor_starter reads values out of the HTCondor configuration files to define the job it should run, as opposed to getting these values from a job ClassAd in the case of a normal HTCondor job. All of the configuration variables names for variables to control things such as the path to the boinc_client binary to use, the command-line arguments, and the initial working directory, are prefixed with the string "BOINC_". Each of these variables is described as either a required or an optional configuration variable.

Required configuration variables:


The full path and executable name of the boinc_client binary to use.


The full path to the local directory where BOINC should run.


The HTCondor universe used for running the boinc_client program. This must be set to vanilla for BOINC to work under HTCondor.


What user the boinc_client program should be run as. This variable is only used if the HTCondor daemons are running as root. In this case, the condor_starter must be told what user identity to switch to before invoking the boinc_client. This can be any valid user on the local system, but it must have write permission in whatever directory is specified by BOINC_InitialDir.

Optional configuration variables:


Command-line arguments that should be passed to the boinc_client program. For example, one way to specify the BOINC project to join is to use the -attach_project argument to specify a project URL and account key. For example:

BOINC_Arguments = --attach_project [account_key]

Environment variables that should be set for the boinc_client.


Full path to the file where stdout from the boinc_client should be written. If this variable is not defined, stdout will be discarded.


Full path to the file where stderr from the boinc_client should be written. If this macro is not defined, stderr will be discarded.

The following example shows one possible usage of these settings:

# Define a shared macro that can be used to define other settings.
# This directory must be manually created before attempting to run
# any backfill jobs.

# Path to the boinc_client to use, and required universe setting
BOINC_Executable = /usr/local/bin/boinc_client
BOINC_Universe = vanilla

# What initial working directory should BOINC use?
BOINC_InitialDir = $(BOINC_HOME)

# Where to place stdout and stderr
BOINC_Output = $(BOINC_HOME)/boinc.out
BOINC_Error = $(BOINC_HOME)/boinc.err

If the HTCondor daemons reading this configuration are running as root, an additional variable must be defined:

# Specify the user that the boinc_client should run as:
BOINC_Owner = nobody

In this case, HTCondor would spawn the boinc_client as nobody, so the directory specified in $(BOINC_HOME) would have to be writable by the nobody user.

A better choice would probably be to create a separate user account just for running BOINC jobs, so that the local BOINC installation is not writable by other processes running as nobody. Alternatively, the BOINC_Owner could be set to daemon.

Attaching to a specific BOINC project

There are a few ways to attach an HTCondor/BOINC installation to a given BOINC project:

  • Use the -attach_project argument to the boinc_client program, defined via the BOINC_Arguments variable. The boinc_client will only accept a single -attach_project argument, so this method can only be used to attach to one project.

  • The boinc_cmd command-line tool can perform various BOINC administrative tasks, including attaching to a BOINC project. Using boinc_cmd, the appropriate argument to use is called -project_attach. Unfortunately, the boinc_client must be running for boinc_cmd to work, so this method can only be used once the HTCondor resource has entered the Backfill state and has spawned the boinc_client.

  • Manually create account files in the local BOINC directory. Upon start up, the boinc_client will scan its local directory (the directory specified with BOINC_InitialDir) for files of the form account_[URL].xml, for example, Any files with a name that matches this convention will be read and processed. The contents of the file define the project URL and the authentication key. The format is:


    For example:


    Of course, the <authenticator> tag would use the real authentication key returned when the account was created at a given project.

    These account files can be copied to the local BOINC directory on all machines in an HTCondor pool, so administrators can either distribute them manually, or use symbolic links to point to a shared file system.

In the two cases of using command-line arguments for boinc_client or running the boinc_cmd tool, BOINC will write out the resulting account file to the local BOINC directory on the machine, and then future invocations of the boinc_client will already be attached to the appropriate project(s).

BOINC on Windows

The Windows version of BOINC has multiple installation methods. The preferred method of installation for use with HTCondor is the Shared Installation method. Using this method gives all users access to the executables. During the installation process

  1. Deselect the option which makes BOINC the default screen saver

  2. Deselect the option which runs BOINC on start up.

  3. Do not launch BOINC at the conclusion of the installation.

There are three major differences from the Unix version to keep in mind when dealing with the Windows installation:

  1. The Windows executables have different names from the Unix versions. The Windows client is called boinc.exe. Therefore, the configuration variable BOINC_Executable is written:

    BOINC_Executable = C:\PROGRA~1\BOINC\boinc.exe

    The Unix administrative tool boinc_cmd is called boinccmd.exe on Windows.

  2. When using BOINC on Windows, the configuration variable BOINC_InitialDir will not be respected fully. To work around this difficulty, pass the BOINC home directory directly to the BOINC application via the BOINC_Arguments configuration variable. For Windows, rewrite the argument line as:

    BOINC_Arguments = --dir $(BOINC_HOME) \
              --attach_project [account_key]

    As a consequence of setting the BOINC home directory, some projects may fail with the authentication error:

    Scheduler request failed: Peer
    certificate cannot be authenticated
    with known CA certificates.

    To resolve this issue, copy the ca-bundle.crt file from the BOINC installation directory to $(BOINC_HOME). This file appears to be project and machine independent, and it can therefore be distributed as part of an automated HTCondor installation.

  3. The BOINC_Owner configuration variable behaves differently on Windows than it does on Unix. Its value may take one of two forms:

    • domain\user

    • user This form assumes that the user exists in the local domain (that is, on the computer itself).

    Setting this option causes the addition of the job attribute

    RunAsUser = True

    to the backfill client. This further implies that the configuration variable STARTER_ALLOW_RUNAS_OWNER be set to True to insure that the local condor_starter be able to run jobs in this manner. For more information on the RunAsUser attribute, see Executing Jobs as the Submitting User. For more information on the the STARTER_ALLOW_RUNAS_OWNER configuration variable, see Shared File System Configuration File Macros.

Per Job PID Namespaces

Per job PID namespaces provide enhanced isolation of one process tree from another through kernel level process ID namespaces. HTCondor may enable the use of per job PID namespaces for Linux RHEL 6, Debian 6, and more recent kernels.

Read about per job PID namespaces

The needed isolation of jobs from the same user that execute on the same machine as each other is already provided by the implementation of slot users as described in User Accounts in HTCondor on Unix Platforms. This is the recommended way to implement the prevention of interference between more than one job submitted by a single user. However, the use of a shared file system by slot users presents issues in the ownership of files written by the jobs.

The per job PID namespace provides a way to handle the ownership of files produced by jobs within a shared file system. It also isolates the processes of a job within its PID namespace. As a side effect and benefit, the clean up of processes for a job within a PID namespace is enhanced. When the process with PID = 1 is killed, the operating system takes care of killing all child processes.

To enable the use of per job PID namespaces, set the configuration to include


This configuration variable defaults to False, thus the use of per job PID namespaces is disabled by default.

Group ID-Based Process Tracking

One function that HTCondor often must perform is keeping track of all processes created by a job. This is done so that HTCondor can provide resource usage statistics about jobs, and also so that HTCondor can properly clean up any processes that jobs leave behind when they exit.

In general, tracking process families is difficult to do reliably. By default HTCondor uses a combination of process parent-child relationships, process groups, and information that HTCondor places in a job’s environment to track process families on a best-effort basis. This usually works well, but it can falter for certain applications or for jobs that try to evade detection.

Jobs that run with a user account dedicated for HTCondor’s use can be reliably tracked, since all HTCondor needs to do is look for all processes running using the given account. Administrators must specify in HTCondor’s configuration what accounts can be considered dedicated via the DEDICATED_EXECUTE_ACCOUNT_REGEXP setting. See User Accounts in HTCondor on Unix Platforms for further details.

Ideally, jobs can be reliably tracked regardless of the user account they execute under. This can be accomplished with group ID-based tracking. This method of tracking requires that a range of dedicated group IDs (GID) be set aside for HTCondor’s use. The number of GIDs that must be set aside for an execute machine is equal to its number of execution slots. GID-based tracking is only available on Linux, and it requires that HTCondor daemons run as root.

GID-based tracking works by placing a dedicated GID in the supplementary group list of a job’s initial process. Since modifying the supplementary group ID list requires root privilege, the job will not be able to create processes that go unnoticed by HTCondor.

Once a suitable GID range has been set aside for process tracking, GID-based tracking can be enabled via the USE_GID_PROCESS_TRACKING parameter. The minimum and maximum GIDs included in the range are specified with the MIN_TRACKING_GID and MAX_TRACKING_GID settings. For example, the following would enable GID-based tracking for an execute machine with 8 slots.


If the defined range is too small, such that there is not a GID available when starting a job, then the condor_starter will fail as it tries to start the job. An error message will be logged stating that there are no more tracking GIDs.

GID-based process tracking requires use of the condor_procd. If USE_GID_PROCESS_TRACKING is true, the condor_procd will be used regardless of the USE_PROCD setting. Changes to MIN_TRACKING_GID and MAX_TRACKING_GID require a full restart of HTCondor.

Cgroup-Based Process Tracking

A new feature in Linux version 2.6.24 allows HTCondor to more accurately and safely manage jobs composed of sets of processes. This Linux feature is called Control Groups, or cgroups for short, and it is available starting with RHEL 6, Debian 6, and related distributions. Documentation about Linux kernel support for cgroups can be found in the Documentation directory in the kernel source code distribution. Another good reference is

The interface between the kernel cgroup functionality is via a (virtual) file system, usually mounted at /sys/fs/cgroup.

If your Linux distribution uses systemd, it will mount the cgroup file system, and the only remaining item is to set configuration variable BASE_CGROUP, as described below.

When cgroups are correctly configured and running, the virtual file system mounted on /sys/fs/cgroup should have several subdirectories under it, and there should an htcondor subdirectory under the directory /sys/fs/cgroup/cpu, /sys/fs/cgroup/memory and some others.

The condor_starter daemon uses cgroups by default on Linux systems to accurately track all the processes started by a job, even when quickly-exiting parent processes spawn many child processes. As with the GID-based tracking, this is only implemented when a condor_procd daemon is running.

Kernel cgroups are named in a virtual file system hierarchy. HTCondor will put each running job on the execute node in a distinct cgroup. The name of this cgroup is the name of the execute directory for that condor_starter, with slashes replaced by underscores, followed by the name and number of the slot. So, for the memory controller, a job running on slot1 would have its cgroup located at /sys/fs/cgroup/memory/htcondor/condor_var_lib_condor_execute_slot1/. The tasks file in this directory will contain a list of all the processes in this cgroup, and many other files in this directory have useful information about resource usage of this cgroup. See the kernel documentation for full details.

Once cgroup-based tracking is configured, usage should be invisible to the user and administrator. The condor_procd log, as defined by configuration variable PROCD_LOG, will mention that it is using this method, but no user visible changes should occur, other than the impossibility of a quickly-forking process escaping from the control of the condor_starter, and the more accurate reporting of memory usage.

A cgroup-enabled HTCondor will install and handle a per-job (not per-process) Linux Out of Memory killer (OOM-Killer). When a job exceeds the memory provisioned by the condor_startd, the Linux kernel will send an OOM message to the condor_starter, and HTCondor will evict the job, and put it on hold. Sometimes, even when the job’s memory usage is below the provisioned amount, if other, non-HTCondor processes, on the system are using too much memory, the linux kernel may choose to OOM-kill the job. In this case, HTCondor will log a message and evict the job, mark it as idle, so it can start again somewhere else.

Limiting Resource Usage Using Cgroups

While the method described to limit a job’s resource usage is portable, and it should run on any Linux or BSD or Unix system, it suffers from one large flaw. The flaw is that resource limits imposed are per process, not per job. An HTCondor job is often composed of many Unix processes. If the method of limiting resource usage with a user job wrapper is used to impose a 2 Gigabyte memory limit, that limit applies to each process in the job individually. If a job created 100 processes, each using just under 2 Gigabytes, the job would continue without the resource limits kicking in. Clearly, this is not what the machine owner intends. Moreover, the memory limit only applies to the virtual memory size, not the physical memory size, or the resident set size. This can be a problem for jobs that use the mmap system call to map in a large chunk of virtual memory, but only need a small amount of memory at one time. Typically, the resource the administrator would like to control is physical memory, because when that is in short supply, the machine starts paging, and can become unresponsive very quickly.

The condor_starter can, using the Linux cgroup capability, apply resource limits collectively to sets of jobs, and apply limits to the physical memory used by a set of processes. The main downside of this technique is that it is only available on relatively new Unix distributions such as RHEL 6 and Debian 6. This technique also may require editing of system configuration files.

To enable cgroup-based limits, first ensure that cgroup-based tracking is enabled, as it is by default on supported systems, as described in section 3.14.13. Once set, the condor_starter will create a cgroup for each job, and set attributes in that cgroup to control memory and cpu usage. These attributes are the cpu.shares attribute in the cpu controller, and two attributes in the memory controller, both memory.limit_in_bytes, and memory.soft_limit_in_bytes. The configuration variable CGROUP_MEMORY_LIMIT_POLICY controls this. If CGROUP_MEMORY_LIMIT_POLICY is set to the string hard, the hard limit will be set to the slot size, and the soft limit to 90% of the slot size.. If set to soft, the soft limit will be set to the slot size and the hard limit will be set to the memory size of the whole startd. By default, this whole size is the detected memory the size, minus RESERVED_MEMORY. Or, if MEMORY is defined, that value is used..

No limits will be set if the value is none. The default is none. If the hard limit is in force, then the total amount of physical memory used by the sum of all processes in this job will not be allowed to exceed the limit. If the process goes above the hard limit, the job will be put on hold.

The memory size used in both cases is the machine ClassAd attribute Memory. Note that Memory is a static amount when using static slots, but it is dynamic when partitionable slots are used. That is, the limit is whatever the “Mem” column of condor_status reports for that slot.

If CGROUP_MEMORY_LIMIT_POLICY is set, HTCondor will also also use cgroups to limit the amount of swap space used by each job. By default, the maximum amount of swap space used by each slot is the total amount of Virtual Memory in the slot, minus the amount of physical memory. Note that HTCondor measures virtual memory in kbytes, and physical memory in megabytes. To prevent jobs with high memory usage from thrashing and excessive paging, and force HTCondor to put them on hold instead, you can tell condor that a job should never use swap, by setting DISABLE_SWAP_FOR_JOB to true (the default is false).

In addition to memory, the condor_starter can also control the total amount of CPU used by all processes within a job. To do this, it writes a value to the cpu.shares attribute of the cgroup cpu controller. The value it writes is copied from the Cpus attribute of the machine slot ClassAd multiplied by 100. Again, like the Memory attribute, this value is fixed for static slots, but dynamic under partitionable slots. This tells the operating system to assign cpu usage proportionally to the number of cpus in the slot. Unlike memory, there is no concept of soft or hard, so this limit only applies when there is contention for the cpu. That is, on an eight core machine, with only a single, one-core slot running, and otherwise idle, the job running in the one slot could consume all eight cpus concurrently with this limit in play, if it is the only thing running. If, however, all eight slots where running jobs, with each configured for one cpu, the cpu usage would be assigned equally to each job, regardless of the number of processes or threads in each job.

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


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 CONCURRENCY_LIMIT_DEFAULT sets this value. For example,


will enforce a limit of at most 1 running job that declares a usage of an unnamed resource. If 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:

concurrency_limits = XSW

This results in the job ClassAd attribute

ConcurrencyLimits = "XSW"

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

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

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:


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 concurrency_limits_expr instead of concurrency_limits . 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


and a machine on "NETWORK_B" configures


The configuration for the negotiator sets the concurrency limits:


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

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


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

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

Submit command concurrency_limits_expr may not be used together with submit command 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.

The VM Universe

vm universe jobs may be executed on any execution site with Xen (via libvirt) or KVM. To do this, HTCondor must be informed of some details of the virtual machine installation, and the execution machines must be configured correctly.

What follows is not a comprehensive list of the options that help set up to use the vm universe; rather, it is intended to serve as a starting point for those users interested in getting vm universe jobs up and running quickly. Details of configuration variables are in the Configuration File Entries Relating to Virtual Machines section.

Begin by installing the virtualization package on all execute machines, according to the vendor’s instructions. We have successfully used Xen and KVM.

For Xen, there are three things that must exist on an execute machine to fully support vm universe jobs.

  1. A Xen-enabled kernel must be running. This running Xen kernel acts as Dom0, in Xen terminology, under which all VMs are started, called DomUs Xen terminology.

  2. The libvirtd daemon must be available, and Xend services must be running.

  3. The pygrub program must be available, for execution of VMs whose disks contain the kernel they will run.

For KVM, there are two things that must exist on an execute machine to fully support vm universe jobs.

  1. The machine must have the KVM kernel module installed and running.

  2. The libvirtd daemon must be installed and running.

Configuration is required to enable the execution of vm universe jobs. The type of virtual machine that is installed on the execute machine must be specified with the VM_TYPE variable. For now, only one type can be utilized per machine. For instance, the following tells HTCondor to use KVM:

VM_TYPE = kvm

The location of the condor_vm-gahp and its log file must also be specified on the execute machine. On a Windows installation, these options would look like this:

VM_GAHP_SERVER = $(SBIN)/condor_vm-gahp.exe

Xen-Specific and KVM-Specific Configuration

Once the configuration options have been set, restart the condor_startd daemon on that host. For example:

$ condor_restart -startd leovinus

The condor_startd daemon takes a few moments to exercise the VM capabilities of the condor_vm-gahp, query its properties, and then advertise the machine to the pool as VM-capable. If the set up succeeded, then condor_status will reveal that the host is now VM-capable by printing the VM type and the version number:

$ condor_status -vm leovinus

After a suitable amount of time, if this command does not give any output, then the condor_vm-gahp is having difficulty executing the VM software. The exact cause of the problem depends on the details of the VM, the local installation, and a variety of other factors. We can offer only limited advice on these matters:

For Xen and KVM, the vm universe is only available when root starts HTCondor. This is a restriction currently imposed because root privileges are required to create a virtual machine on top of a Xen-enabled kernel. Specifically, root is needed to properly use the libvirt utility that controls creation and management of Xen and KVM guest virtual machines. This restriction may be lifted in future versions, depending on features provided by the underlying tool libvirt.

When a vm Universe Job Fails to Start

If a vm universe job should fail to launch, HTCondor will attempt to distinguish between a problem with the user’s job description, and a problem with the virtual machine infrastructure of the matched machine. If the problem is with the job, the job will go on hold with a reason explaining the problem. If the problem is with the virtual machine infrastructure, HTCondor will reschedule the job, and it will modify the machine ClassAd to prevent any other vm universe job from matching. vm universe configuration is not slot-specific, so this change is applied to all slots.

When the problem is with the virtual machine infrastructure, these machine ClassAd attributes are changed:

  • HasVM will be set to False

  • VMOfflineReason will be set to a somewhat explanatory string

  • VMOfflineTime will be set to the time of the failure

  • OfflineUniverses will be adjusted to include "VM" and 13

Since condor_submit adds HasVM == True to a vm universe job’s requirements, no further vm universe jobs will match.

Once any problems with the infrastructure are fixed, to change the machine ClassAd attributes such that the machine will once again match to vm universe jobs, an administrator has three options. All have the same effect of setting the machine ClassAd attributes to the correct values such that the machine will not reject matches for vm universe jobs.

  1. Restart the condor_startd daemon.

  2. Submit a vm universe job that explicitly matches the machine. When the job runs, the code detects the running job and causes the attributes related to the vm universe to be set indicating that vm universe jobs can match with this machine.

  3. Run the command line tool condor_update_machine_ad to set machine ClassAd attribute HasVM to True, and this will cause the other attributes related to the vm universe to be set indicating that vm universe jobs can match with this machine. See the condor_update_machine_ad manual page for examples and details.

Using HTCondor with AFS

Configuration variables that allow machines to interact with and use a shared file system are given at the Shared File System Configuration File Macros section.

Limitations with AFS occur because HTCondor does not currently have a way to authenticate itself to AFS. This is true of the HTCondor daemons that would like to authenticate as the AFS user condor, and of the condor_shadow which would like to authenticate as the user who submitted the job it is serving. Since neither of these things can happen yet, there are special things to do when interacting with AFS. Some of this must be done by the administrator(s) installing HTCondor. Other things must be done by HTCondor users who submit jobs.

AFS and HTCondor for Users

The condor_shadow daemon runs on the machine where jobs are submitted. It performs all file system access on behalf of the jobs. Because the condor_shadow daemon is not authenticated to AFS as the user who submitted the job, the condor_shadow daemon will not normally be able to write any output. Therefore the directories in which the job will be creating output files will need to be world writable; they need to be writable by non-authenticated AFS users. In addition, the program’s stdout, stderr, log file, and any file the program explicitly opens will need to be in a directory that is world-writable.

An administrator may be able to set up special AFS groups that can make unauthenticated access to the program’s files less scary. For example, there is supposed to be a way for AFS to grant access to any unauthenticated process on a given host. If set up, write access need only be granted to unauthenticated processes on the access point, as opposed to any unauthenticated process on the Internet. Similarly, unauthenticated read access could be granted only to processes running on the access point.

A solution to this problem is to not use AFS for output files. If disk space on the access point is available in a partition not on AFS, submit the jobs from there. While the condor_shadow daemon is not authenticated to AFS, it does run with the effective UID of the user who submitted the jobs. So, on a local (or NFS) file system, the condor_shadow daemon will be able to access the files, and no special permissions need be granted to anyone other than the job submitter. If the HTCondor daemons are not invoked as root however, the condor_shadow daemon will not be able to run with the submitter’s effective UID, leading to a similar problem as with files on AFS.

AFS and HTCondor for Administrators

The largest result from the lack of authentication with AFS is that the directory defined by the configuration variable LOCAL_DIR and its subdirectories log and spool on each machine must be either writable to unauthenticated users, or must not be on AFS. Making these directories writable a very bad security hole, so it is not a viable solution. Placing LOCAL_DIR onto NFS is acceptable. To avoid AFS, place the directory defined for LOCAL_DIR on a local partition on each machine in the pool. This implies running condor_configure to install the release directory and configure the pool, setting the LOCAL_DIR variable to a local partition. When that is complete, log into each machine in the pool, and run condor_init to set up the local HTCondor directory.

The directory defined by RELEASE_DIR, which holds all the HTCondor binaries, libraries, and scripts, can be on AFS. None of the HTCondor daemons need to write to these files. They only need to read them. So, the directory defined by RELEASE_DIR only needs to be world readable in order to let HTCondor function. This makes it easier to upgrade the binaries to a newer version at a later date, and means that users can find the HTCondor tools in a consistent location on all the machines in the pool. Also, the HTCondor configuration files may be placed in a centralized location.

Finally, consider setting up some targeted AFS groups to help users deal with HTCondor and AFS better. This is discussed in the following manual subsection. In short, create an AFS group that contains all users, authenticated or not, but which is restricted to a given host or subnet. These should be made as host-based ACLs with AFS, but here at UW-Madison, we have had some trouble getting that working. Instead, we have a special group for all machines in our department. The users here are required to make their output directories on AFS writable to any process running on any of our machines, instead of any process on any machine with AFS on the Internet.