The following sections describe how to set up HTCondor for use in special environments or configurations.
Configuration variables that allow machines to interact with and use a shared file system are given at section 3.3.7.
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.
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. This is what we do for the UW-Madison's CS department HTCondor pool, and it works quite well.
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.
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 submit machine, as opposed to any unauthenticated process on the Internet. Similarly, unauthenticated read access could be granted only to processes running on the submit machine.
A solution to this problem is to not use AFS for output files. If disk space on the submit machine 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.
Because staging data on the submit machine is not always efficient, HTCondor permits input files to be transferred from a location specified by a URL; likewise, output files may be transferred to a location specified by a URL. All transfers (both input and output) are accomplished by invoking a plug-in, an executable or shell script that handles the task of file transfer.
For transferring input files, URL specification is limited to jobs running under the vanilla universe and to a vm universe VM image file. The execute machine retrieves the files. This differs from the normal file transfer mechanism, in which transfers are from the machine where the job is submitted to the machine where the job is executed. Each file to be transferred by specifying a URL, causing a plug-in to be invoked, is specified separately in the job submit description file with the command transfer_input_files; see section 2.5.4 for details.
For transferring output files, either the entire output sandbox, which are all files produced or modified by the job as it executes, or a subset of these files, as specified by the submit description file command transfer_output_files are transferred to the directory specified by the URL. The URL itself is specified in the separate submit description file command output_destination; see section 2.5.4 for details. The plug-in is invoked once for each output file to be transferred.
Configuration identifies the availability of the one or more plug-in(s). The plug-ins must be installed and available on every execute machine that may run a job which might specify a URL, either for input or for output.
URL transfers are enabled by default in the configuration of execute machines. Disabling URL transfers is accomplished by setting
ENABLE_URL_TRANSFERS = FALSE
A comma separated list giving the absolute path and name of all available plug-ins is specified as in the example:
FILETRANSFER_PLUGINS = /opt/condor/plugins/wget-plugin, \
/opt/condor/plugins/hdfs-plugin, \
/opt/condor/plugins/custom-plugin
The condor_starter invokes all listed plug-ins to determine their capabilities. Each may handle one or more protocols (scheme names). The plug-in's response to invocation identifies which protocols it can handle. When a URL transfer is specified by a job, the condor_starter invokes the proper one to do the transfer. If more than one plugin is capable of handling a particular protocol, then the last one within the list given by FILETRANSFER_PLUGINS is used.
HTCondor assumes that all plug-ins will respond in specific ways. To determine the capabilities of the plug-ins as to which protocols they handle, the condor_starter daemon invokes each plug-in giving it the command line argument -classad. In response to invocation with this command line argument, the plug-in must respond with an output of three ClassAd attributes. The first two are fixed:
PluginVersion = "0.1" PluginType = "FileTransfer"
The third ClassAd attribute is SupportedMethods. This attribute is a string containing a comma separated list of the protocols that the plug-in handles. So, for example
SupportedMethods = "http,ftp,file"would identify that the three protocols described by
http,
ftp, and file are supported.
These strings will match the protocol specification as given
within a URL in a transfer_input_files command
or within a URL in an output_destination command
in a submit description file for a job.
When a job specifies a URL transfer, the plug-in is invoked, without the command line argument -classad. It will instead be given two other command line arguments. For the transfer of input file(s), the first will be the URL of the file to retrieve and the second will be the absolute path identifying where to place the transferred file. For the transfer of output file(s), the first will be the absolute path on the local machine of the file to transfer, and the second will be the URL of the directory and file name at the destination.
The plug-in is expected to do the transfer, exiting with status 0 if the transfer was successful, and a non-zero status if the transfer was not successful. When not successful, the job is placed on hold, and the job ClassAd attribute HoldReason will be set as appropriate for the job. The job ClassAd attribute HoldReasonSubCode will be set to the exit status of the plug-in.
As an example of the transfer of a subset of output files, assume that the submit description file contains
output_destination = url://server/some/directory/ transfer_output_files = foo, bar, quxHTCondor invokes the plug-in that handles the url protocol three times. The directory delimiter (
/ on Unix, and \ on Windows)
is appended to the destination URL,
such that the three (Unix) invocations of the plug-in will appear similar to
url_plugin /path/to/local/copy/of/foo url://server/some/directory//foo url_plugin /path/to/local/copy/of/bar url://server/some/directory//bar url_plugin /path/to/local/copy/of/qux url://server/some/directory//qux
Note that this functionality is not limited to a predefined set
of protocols.
New ones can be invented.
As an invented example,
the zkm transfer type writes random bytes to a file.
The plug-in that handles zkm transfers would respond to
invocation with the -classad command line argument with:
PluginVersion = "0.1" PluginType = "FileTransfer" SupportedMethods = "zkm"And, then when a job requested that this plug-in be invoked, for the invented example:
transfer_input_files = zkm://128/r-datathe plug-in will be invoked with a first command line argument of
zkm://128/r-data and a second command line argument giving
the full path along with the file name r-data as the location
for the plug-in to write 128 bytes of random data.
The transfer of output files in this manner was introduced in HTCondor version 7.6.0. Incompatibility and inability to function will result if the executables for the condor_starter and condor_shadow are versions earlier than HTCondor version 7.6.0. Here is the expected behavior for these cases that cannot be backward compatible.
A single, global configuration file may be used for all platforms in an HTCondor pool, with only platform-specific settings placed in separate files. This greatly simplifies administration of a heterogeneous pool by allowing changes 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 regular condor_config file, which would be shared by all platforms. This global file would be the one that is found with the CONDOR_CONFIG environment variable, the user condor's home directory, or /etc/condor/condor_config. 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 order of file specification in the LOCAL_CONFIG_FILE configuration variable is important, because settings in files at the beginning of the list are overridden if the same settings occur in files later within the list. So, if specifying the platform-specific file and then the machine-specific file, settings in the machine-specific file would override those in the platform-specific file (as is likely desired).
The name of platform-specific configuration files may be specified by using the ARCH and OPSYS configuration variables, as are 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 condor_config.X86_64.LINUX
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, the LOCAL_CONFIG_FILE configuration macro should be:
LOCAL_CONFIG_FILE = $(ETC)/condor_config.$(ARCH).$(OPSYS), \
$(ETC)/$(HOSTNAME).local
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, \
$(ETC)/$(HOSTNAME).local
The configuration variables that are truly platform-specific are:
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.
It is certainly possible that an installation may want other configuration variables to be platform-specific as well. 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.
In order to take advantage of two major HTCondor features: checkpointing and remote system calls, users need to relink their binaries. Programs that are not relinked for HTCondor can run under HTCondor's vanilla universe. However, these jobs cannot take checkpoints and migrate.
To relink programs with HTCondor, we provide the condor_compile tool. As installed by default, condor_compile works with the following commands: gcc, g++, g77, cc, acc, c89, CC, f77, fort77, ld. See the condor_compile(1) man page for details on using condor_compile.
condor_compile can work transparently with all commands on the system, including make. The basic idea here is to replace the system linker (ld) with the HTCondor linker. Then, when a program is to be linked, the HTCondor linker figures out whether this binary will be for HTCondor, or for a normal binary. If it is to be a normal compile, the old ld is called. If this binary is to be linked for HTCondor, the script performs the necessary operations in order to prepare a binary that can be used with HTCondor. In order to differentiate between normal builds and HTCondor builds, the user simply places condor_compile before their build command, which sets the appropriate environment variable that lets the HTCondor linker script know it needs to do its magic.
In order to perform this full installation of condor_compile, the following steps need to be taken:
The actual commands to execute depend upon the platform. The location of the system linker (ld), is as follows:
Operating System Location of ld (ld-path) Linux /usr/bin
On these platforms, issue the following commands (as root), where ld-path is replaced by the path to the system's ld.
mv /[ld-path]/ld /<ld-path>/ld.real cp /usr/local/condor/lib/ld /<ld-path>/ld chown root /<ld-path>/ld chmod 755 /<ld-path>/ld
If you remove HTCondor from your system later on, linking will continue to work, since the HTCondor linker will always default to compiling normal binaries and simply call the real ld. In the interest of simplicity, it is recommended that you reverse the above changes by moving your ld.real linker back to its former position as ld, overwriting the HTCondor linker.
NOTE: If you ever upgrade your operating system after performing a full installation of condor_compile, you will probably have to re-do all the steps outlined above. Generally speaking, new versions or patches of an operating system might replace the system ld binary, which would undo the full installation of condor_compile.
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.
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.
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/.kbdd.pid
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 ~/.kbdd.pid, so that when the user logs out, XDM can do:
condor_kbdd -k $HOME/.kbdd.pid
This will shut down the process recorded in file ~/.kbdd.pid 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.
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.
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
section 3.3.16 on
page ![[*]](crossref.png)
.
Here are brief explanations of the entries that must be customized:
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.
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 COLLECTOR to 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 = $(COLLECTOR) VIEW_SERVER_ARGS = -f -p 12345 VIEW_SERVER_ENVIRONMENT = "_CONDOR_COLLECTOR_LOG=$(LOG)/ViewServerLog" DAEMON_LIST = MASTER, NEGOTIATOR, COLLECTOR, VIEW_SERVER
For this change to take effect, restart the 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.
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:
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 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:
condor_status -pool condor.view.host[:portnumber]
A condor_collector may also be configured to report to multiple HTCondorView servers. The configuration variable 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:
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 CONDOR_VIEW_HOST for VIEWSERV01 and VIEWSERV02 is unset, to prevent them from inheriting the global value of 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 CONDOR_VIEW_HOST, this precaution is not required.
HTCondor jobs are formed from executables that are compiled to execute on specific platforms. This in turn restricts the machines within an HTCondor pool where a job may be executed. An HTCondor job may now be executed on a virtual machine running VMware, Xen, or KVM. This allows Windows executables to run on a Linux machine, and Linux executables to run on a Windows machine.
In older versions of HTCondor, other parts of the system were also referred to as virtual machines, but in all cases, those are now known as slots. A virtual machine here describes the environment in which the outside operating system (called the host) emulates an inner operating system (called the inner virtual machine), such that an executable appears to run directly on the inner virtual machine. In other parts of HTCondor, a slot (formerly known as virtual machine) refers to the multiple cores of a multi-core machine. Also, be careful not to confuse the virtual machines discussed here with the Java Virtual Machine (JVM) referenced in other parts of this manual. Targeting an HTCondor job to run on an inner virtual machine is also different than using the vm universe. The vm universe lands and starts up a virtual machine instance, which is the HTCondor job, on an execute machine.
HTCondor has the flexibility to run a job on either the host or the inner virtual machine, hence two platforms appear to exist on a single machine. Since two platforms are an illusion, HTCondor understands the illusion, allowing an HTCondor job to be executed on only one at a time.
HTCondor must be separately installed, separately configured, and separately running on both the host and the inner virtual machine.
The configuration for the host specifies VMP_VM_LIST . This specifies host names or IP addresses of all inner virtual machines running on this host. An example configuration on the host machine:
VMP_VM_LIST = vmware1.domain.com, vmware2.domain.com
The configuration for each separate inner virtual machine specifies VMP_HOST_MACHINE . This specifies the host for the inner virtual machine. An example configuration on an inner virtual machine:
VMP_HOST_MACHINE = host.domain.com
Given this configuration, as well as communication between HTCondor daemons running on the host and on the inner virtual machine, the policy for when jobs may execute is set by HTCondor. While the host is executing an HTCondor job, the START policy on the inner virtual machine is overridden with False, so no HTCondor jobs will be started on the inner virtual machine. Conversely, while the inner virtual machine is executing an HTCondor job, the START policy on the host is overridden with False, so no HTCondor jobs will be started on the host.
The inner virtual machine is further provided with a new syntax for referring to the machine ClassAd attributes of its host. Any machine ClassAd attribute with a prefix of the string HOST_ explicitly refers to the host's ClassAd attributes. The START policy on the inner virtual machine ought to use this syntax to avoid starting jobs when its host is too busy processing other items. An example configuration for START on an inner virtual machine:
START = ( (KeyboardIdle > 150 ) && ( HOST_KeyboardIdle > 150 ) \
&& ( LoadAvg <= 0.3 ) && ( HOST_TotalLoadAvg <= 0.3 ) )
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.
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 submit machine,
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 submit machine apply to
this machine, such as having enough disk space in the spool
directory to hold jobs. See section 3.2.2 on
page for details on these issues.
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 = "DedicatedScheduler@full.host.name" STARTD_ATTRS = $(STARTD_ATTRS), DedicatedScheduler
Substitute the host name of the dedicated scheduler
machine for the string "full.host.name".
If running personal HTCondor, the name of the scheduler includes the user name it was started as, so the configuration appears as:
DedicatedScheduler = "DedicatedScheduler@username@full.host.name" 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 = "DedicatedScheduler@full.host.name"The host name of the dedicated scheduler substitutes for the string
full.host.name.
Different resources in the pool may have different dedicated policies by varying the local configuration.
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 CONTINUE = True PREEMPT = False KILL = False WANT_SUSPEND = 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.
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
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 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.
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, as HTCondor cannot produce checkpoints for parallel universe jobs, 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:
STARTD_JOB_EXPRS = JobPrio SCHEDD_PREEMPTION_REQUIREMENTS = (My.JobPrio < Target.JobPrio) SCHEDD_PREEMPTION_RANK = 0.0
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.
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.
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 http://boinc.berkeley.edu.
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.
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
section 3.5 beginning on
page , especially
sections 3.5.5, 3.5.6, and
3.5.7.
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.
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
section 3.3.10 on
page .
The following example shows a possible configuration to enable backfill:
# Turn on backfill functionality, and use BOINC ENABLE_BACKFILL = TRUE BACKFILL_SYSTEM = 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)
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: http://boinc.berkeley.edu. 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 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 (each computation is referred to as a project using BOINC's terminology), instead of always working on a hard coded computation. 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 client software (a program called 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.
If a working installation of BOINC currently exists on machines where backfill is desired, skip the remainder of this section. Continue reading with the section titled ``Configuring the BOINC client under HTCondor''.
In HTCondor Version 8.0.1, the BOINC client software that actually spawns and manages the backfill computations (the boinc_client) must be manually downloaded, installed and configured outside of HTCondor. Hopefully in future versions, the HTCondor package will include the boinc_client, and there will be a way to automatically install and configure the BOINC software together with HTCondor.
The boinc_client executables can be obtained at http://boinc.berkeley.edu/download.php.
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 via an HTCondor configuration setting, BOINC_Executable , described below.
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, just like the spool or execute directories used by HTCondor. This location of this directory is defined using the BOINC_InitialDir macro, described below. 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 setting described below.
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 software spawned by HTCondor (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 http://einstein.phys.uwm.edu/create_account_form.php, fill in the web form, and generate a new Einstein@home identity. This identity takes the form of a project URL (such as http://einstein.phys.uwm.edu) 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 (described in the next section).
This section assumes that the BOINC client software has already been installed on a given machine, that the BOINC projects to join have been selected, and that a unique project account key has been created for each project. If any of these steps has not been completed, please read the previous section titled ``Installing the BOINC client software''
Whenever the condor_startd decides to spawn the boinc_client
to perform backfill computations (when ENABLE_BACKFILL is
True, when the resource is in Unclaimed/Idle, and when the
START_BACKFILL expression evaluates to True), 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 spawn normal HTCondor
jobs.
In fact, the argv[0] of the boinc_client will be renamed to
``condor_exec'', as described in section 2.14.1 on
page .
The condor_starter for spawning the boinc_client reads values out of the HTCondor configuration files to define the job it should run, as opposed to getting these values from a job classified ad in the case of a normal HTCondor job. All of the configuration settings to control things like the path to the boinc_client binary to use, the command-line arguments, the initial working directory, and so on, are prefixed with the string "BOINC_". Each possible setting is described below:
Required settings:
Optional settings:
BOINC_Arguments = --attach_project http://einstein.phys.uwm.edu [account_key]
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. BOINC_HOME = $(LOCAL_DIR)/boinc # 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) # Save 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 macro 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:
<account> <master_url>[URL]</master_url> <authenticator>[key]</authenticator> </account>
For example:
<account> <master_url>http://einstein.phys.uwm.edu</master_url> <authenticator>aaaa1111bbbb2222cccc3333</authenticator> </account>
(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 first two cases (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).
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
There are three major differences from the Unix version to keep in mind when dealing with the Windows installation:
BOINC_Executable = C:\PROGRA~1\BOINC\boinc.exe
The Unix administrative tool boinc_cmd is called boinccmd.exe on Windows.
BOINC_Arguments = --dir $(BOINC_HOME) \
--attach_project http://einstein.phys.uwm.edu [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.
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 = Trueto 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 section 7.2.4. For more information on the the STARTER_ALLOW_RUNAS_OWNER configuration variable, see section 3.3.7.
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 5, and more recent kernels.
Read about per job PID namespaces http://lwn.net/Articles/531419/.
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 section 3.6.13. 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
USE_PID_NAMESPACES = True
This configuration variable defaults to False, thus the use of per job PID namespaces is disabled by default.
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 Section 3.6.13 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 either runs as root or uses privilege separation (see Section 3.6.14).
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.
USE_GID_PROCESS_TRACKING = True MIN_TRACKING_GID = 750 MAX_TRACKING_GID = 757
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.
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 http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Resource_Management_Guide/index.html Even if cgroup support is built into the kernel, many distributions do not install the cgroup tools by default. In order to use cgroups, the tools must be installed. On RPM-based systems, these can be installed with the command
yum install libcgroup\*
After these tools are installed, the cgconfig service needs to be running. It parses the /etc/cgconfig.conf file, and makes appropriate mounts under /cgroup. Before starting the cgconfig service, you will need to edit the file /etc/cgconfig.conf to add a group specific to HTCondor.
Here is an example of the contents of file /etc/cgconfig.conf with appropriate values for the HTCondor group:
mount {
cpu = /cgroup/cpu;
cpuset = /cgroup/cpuset;
cpuacct = /cgroup/cpuacct;
memory = /cgroup/memory;
freezer = /cgroup/freezer;
blkio = /cgroup/blkio;
}
group htcondor {
cpu {}
cpuacct {}
memory {}
freezer {}
blkio {}
}
After the /etc/cgconfig.conf file has had the htcondor group added to it, add and start the cgconfig service by running
chkconfig --add cgconfig service cgconfig start
When the cgconfig service is correctly running, the virtual filesystem mounted on /cgroup should have several subdirectories under it, and there should a an htcondor subdirectory under the directory /cgroup/cpu
Starting with HTCondor version 7.7.0, the condor_starter daemon can optionally use cgroups 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. The HTCondor team recommends enabling this feature on Linux platforms that support it. When cgroup tracking is enabled, HTCondor is able to report a much more accurate measurement of the physical memory used by a set of processes.
To enable cgroup tracking in HTCondor, once cgroups have been enabled in the operating system, set the BASE_CGROUP configuration variable to the string that matches the group name specified in the /etc/cgconfig.conf In the example above, "htcondor" is a good choice. There is no default value for BASE_CGROUP , and if left unset, cgroup tracking will not be used.
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 /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.
An administrator can strictly limit the usage of system resources by jobs for any job that may be wrapped using the script defined by the configuration variable USER_JOB_WRAPPER . These are jobs within universes that are controlled by the condor_starter daemon, and they include the vanilla, standard, java, local, and parallel universes.
The job's ClassAd is written by the condor_starter daemon. It will need to contain attributes that the script defined by USER_JOB_WRAPPER can use to implement platform specific resource limiting actions. Examples of resources that may be referred to for limiting purposes are RAM, swap space, file descriptors, stack size, and core file size.
An initial sample of a USER_JOB_WRAPPER script is provided in the installation at $(LIBEXEC)/condor_limits_wrapper.sh. Here is the contents of that file:
#!/bin/sh
# Copyright 2008 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
if [[ $_CONDOR_MACHINE_AD != "" ]]; then
mem_limit=$((`egrep '^Memory' $_CONDOR_MACHINE_AD | cut -d ' ' -f 3` * 1024))
# block_size=$((`stat -f -c %s .` / 1024))
# disk_limit=$((`egrep '^Disk' $_CONDOR_MACHINE_AD | cut -d ' ' -f 3` / $block_size))
disk_limit=`egrep '^Disk' $_CONDOR_MACHINE_AD | cut -d ' ' -f 3`
vm_limit=`egrep '^VirtualMemory' $_CONDOR_MACHINE_AD | cut -d ' ' -f 3`
ulimit -d $mem_limit
if [[ $? != 0 ]] || [[ $mem_limit = "" ]]; then
echo "Failed to set Memory Resource Limit" > $_CONDOR_WRAPPER_ERROR_FILE
exit 1
fi
ulimit -f $disk_limit
if [[ $? != 0 ]] || [[ $disk_limit = "" ]]; then
echo "Failed to set Disk Resource Limit" > $_CONDOR_WRAPPER_ERROR_FILE
exit 1
fi
ulimit -v $vm_limit
if [[ $? != 0 ]] || [[ $vm_limit = "" ]]; then
echo "Failed to set Virtual Memory Resource Limit" > $_CONDOR_WRAPPER_ERROR_FILE
exit 1
fi
fi
exec "$@"
error=$?
echo "Failed to exec($error): $@" > $_CONDOR_WRAPPER_ERROR_FILE
exit 1
If used in an unmodified form, this script sets the job's limits on a per slot basis for memory, disk, and virtual memory usage, with the limits defined by the values in the machine ClassAd. This example file will need to be modified and merged for use with a preexisting USER_JOB_WRAPPER script.
If additional functionality is added to the script, an administrator is likely to use the USER_JOB_WRAPPER script in conjunction with SUBMIT_EXPRS to force the job ClassAd to contain attributes that the USER_JOB_WRAPPER script expects to have defined.
The following variables are set in the environment of the the USER_JOB_WRAPPER script by the condor_starter daemon, when the USER_JOB_WRAPPER is defined.
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 enable cgroup-based tracking, as described in section 3.12.12. Once that is set, the condor_starter will create a cgroup for each job, and set two attributes in that cgroup which control resource usage therein. These two attributes are the cpu.shares attribute in the cpu controller, and one of two attributes in the memory controller, either memory.limit_in_bytes, or memory.soft_limit_in_bytes. The configuration variable CGROUP_MEMORY_LIMIT_POLICY controls whether the hard limit (the former) or the soft limit will be used. If CGROUP_MEMORY_LIMIT_POLICY is set to the string hard, the hard limit will be used. If set to soft, the soft limit will be used. Otherwise, no limit 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 processes try to allocate more memory, the allocation will succeed, and virtual memory will be allocated, but no additional physical memory will be allocated. The system will keep the amount of physical memory constant by swapping some page from that job out of memory. However, if the soft limit is in place, the job will be allowed to go over the limit if there is free memory available on the system. Only when there is contention between other processes for physical memory will the system force physical memory into swap and push the physical memory used towards the assigned limit. 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.
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. 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 in each job.
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. This is useful in the case of a shared resource, like an NFS or database server that some jobs use, and 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 job submit file which resources the job consumes.
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. Pick the name XSW as the name of the resource:
XSW_LIMIT = 3where "XSW" is the invented name of this resource, 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,
CONCURRENCY_LIMIT_DEFAULT = 1will 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 of resources for which there is no specifically named concurrency limits.
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 = XSWThis 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, by default each job consumes one unit of resource. Jobs can declare that they use more than one unit, by following the resource name by a colon character and the integer number of resources. For example, if the above job uses three times as many fileserver resources as a baseline job, this could be declared as follows:
concurrency_limits = XSW, DATABASE, FILESERVER:3
If there are many different types of resources which all have the same default capacity, it may be tedious to define them all individually. A shortcut to defining sets of consumable resources exists, by using a common prefix. After the common prefix, a period follows, then the rest of the resource name.
CONCURRENCY_LIMIT_DEFAULT = 5 CONCURRENCY_LIMIT_DEFAULT_LARGE = 100 CONCURRENCY_LIMIT_DEFAULT_SMALL = 25
The above configuration defines the prefixes named large and small, along with the default limit of 5. With this configuration a concurrency limit named "large.swlicense" will have a default limit of 100. A concurrency limit named "large.dbsession" will also have a limit of 100, as will any resource that begins with "large." A limit named "small.dbsession" will receive the default limit of 25. A concurrency limit named "other.license" will receive the global default limit of 5, as there is no value set for for CONCURRENCY_LIMIT_DEFAULT_OTHER.
Note that it is possible, in 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.