3.1 Introduction

This is the Condor Administrator's Manual for Unix. Its purpose is to aid in the installation and administration of a Condor pool. For help on using Condor, see the Condor User's Manual.

A Condor pool is comprised of a single machine which serves as the central manager, and an arbitrary number of other machines that have joined the pool. Conceptually, the pool is a collection of resources (machines) and resource requests (jobs). The role of Condor is to match waiting requests with available resources. Every part of Condor sends periodic updates to the central manager, the centralized repository of information about the state of the pool. Periodically, the central manager assesses the current state of the pool and tries to match pending requests with the appropriate resources.

Each resource has an owner, the user who works at the machine. This person has absolute power over their own resource and Condor goes out of its way to minimize the impact on this owner caused by Condor. It is up to the resource owner to define a policy for when Condor requests will serviced and when they will be denied.

Each resource request has an owner as well: the user who submitted the job. These people want Condor to provide as many CPU cycles as possible for their work. Often the interests of the resource owners are in conflict with the interests of the resource requesters.

The job of the Condor administrator is to configure the Condor pool to find the happy medium that keeps both resource owners and users of resources satisfied. The purpose of this manual is to help you understand the mechanisms that Condor provides to enable you to find this happy medium for your particular set of users and resource owners.

3.1.1 The Different Roles a Machine Can Play

Every machine in a Condor pool can serve a variety of roles. Most machines serve more than one role simultaneously. Certain roles can only be performed by single machines in your pool. The following list describes what these roles are and what resources are required on the machine that is providing that service:

Central Manager

There can be only one central manager for your pool. The machine is the collector of information, and the negotiator between resources and resource requests. These two halves of the central manager's responsibility are performed by separate daemons, so it would be possible to have different machines providing those two services. However, normally they both live on the same machine. This machine plays a very important part in the Condor pool and should be reliable. If this machine crashes, no further matchmaking can be performed within the Condor system (although all current matches remain in effect until they are broken by either party involved in the match). Therefore, choose for central manager a machine that is likely to be up and running all the time, or at least one that will be rebooted quickly if something goes wrong. The central manager will ideally have a good network connection to all the machines in your pool, since they all send updates over the network to the central manager. All queries go to the central manager.

Execute

Any machine in your pool (including your Central Manager) can be configured for whether or not it should execute Condor jobs. Obviously, some of your machines will have to serve this function or your pool won't be very useful. Being an execute machine doesn't require many resources at all. About the only resource that might matter is disk space, since if the remote job dumps core, that file is first dumped to the local disk of the execute machine before being sent back to the submit machine for the owner of the job. However, if there isn't much disk space, Condor will simply limit the size of the core file that a remote job will drop. In general the more resources a machine has (swap space, real memory, CPU speed, etc.) the larger the resource requests it can serve. However, if there are requests that don't require many resources, any machine in your pool could serve them.

Submit

Any machine in your pool (including your Central Manager) can be configured for whether or not it should allow Condor jobs to be submitted. The resource requirements for a submit machine are actually much greater than the resource requirements for an execute machine. First of all, every job that you submit that is currently running on a remote machine generates another process on your submit machine. So, if you have lots of jobs running, you will need a fair amount of swap space and/or real memory. In addition all the checkpoint files from your jobs are stored on the local disk of the machine you submit from. Therefore, if your jobs have a large memory image and you submit a lot of them, you will need a lot of disk space to hold these files. This disk space requirement can be somewhat alleviated with a checkpoint server (described below), however the binaries of the jobs you submit are still stored on the submit machine.

Checkpoint Server

One machine in your pool can be configured as a checkpoint server. This is optional, and is not part of the standard Condor binary distribution. The checkpoint server is a centralized machine that stores all the checkpoint files for the jobs submitted in your pool. This machine should have lots of disk space and a good network connection to the rest of your pool, as the traffic can be quite heavy.

Now that you know the various roles a machine can play in a Condor pool, we will describe the actual daemons within Condor that implement these functions.

3.1.2 The Condor Daemons

The following list describes all the daemons and programs that could be started under Condor and what they do:

condor_master

This daemon is responsible for keeping all the rest of the Condor daemons running on each machine in your pool. It spawns the other daemons, and periodically checks to see if there are new binaries installed for any of them. If there are, the master will restart the affected daemons. In addition, if any daemon crashes, the master will send e-mail to the Condor Administrator of your pool and restart the daemon. The condor_master also supports various administrative commands that let you start, stop or reconfigure daemons remotely. The condor_master will run on every machine in your Condor pool, regardless of what functions each machine are performing.

condor_startd

This daemon represents a given resource (namely, a machine capable of running jobs) to the Condor pool. It advertises certain attributes about that resource that are used to match it with pending resource requests. The startd will run on any machine in your pool that you wish to be able to execute jobs. It is responsible for enforcing the policy that resource owners configure which determines under what conditions remote jobs will be started, suspended, resumed, vacated, or killed. When the startd is ready to execute a Condor job, it spawns the condor_starter, described below.

condor_starter

This program is the entity that actually spawns the remote Condor job on a given machine. It sets up the execution environment and monitors the job once it is running. When a job completes, the starter notices this, sends back any status information to the submitting machine, and exits.

condor_schedd

This daemon represents resource requests to the Condor pool. Any machine that you wish to allow users to submit jobs from needs to have a condor_schedd running. When users submit jobs, they go to the schedd, where they are stored in the job queue, which the schedd manages. Various tools to view and manipulate the job queue (such as condor_submit, condor_q, or condor_rm) all must connect to the schedd to do their work. If the schedd is down on a given machine, none of these commands will work.

The condor_schedd advertises the number of waiting jobs in its job queue and is responsible for claiming available resources to serve those requests. Once a schedd has been matched with a given resource, the schedd spawns a condor_shadow (described below) to serve that particular request.

condor_shadow

This program runs on the machine where a given request was submitted and acts as the resource manager for the request. Jobs that are linked for Condor's standard universe, which perform remote system calls, do so via the condor_shadow. Any system call performed on the remote execute machine is sent over the network, back to the condor_shadow which actually performs the system call (such as file I/O) on the submit machine, and the result is sent back over the network to the remote job. In addition, the shadow is responsible for making decisions about the request (such as where checkpoint files should be stored, how certain files should be accessed, etc).

condor_collector

This daemon is responsible for collecting all the information about the status of a Condor pool. All other daemons periodically send ClassAd updates to the collector. These ClassAds contain all the information about the state of the daemons, the resources they represent or resource requests in the pool (such as jobs that have been submitted to a given schedd). The condor_status command can be used to query the collector for specific information about various parts of Condor. In addition, the Condor daemons themselves query the collector for important information, such as what address to use for sending commands to a remote machine.

condor_negotiator

This daemon is responsible for all the match-making within the Condor system. Periodically, the negotiator begins a negotiation cycle, where it queries the collector for the current state of all the resources in the pool. It contacts each schedd that has waiting resource requests in priority order, and tries to match available resources with those requests. The negotiator is responsible for enforcing user priorities in the system, where the more resources a given user has claimed, the less priority they have to acquire more resources. If a user with a better priority has jobs that are waiting to run, and resources are claimed by a user with a worse priority, the negotiator can preempt that resource and match it with the user with better priority.

NOTE: A higher numerical value of the user priority in Condor translate into worse priority for that user. The best priority you can have is 0.5, the lowest numerical value, and your priority gets worse as this number grows.

condor_kbdd

This daemon is used on Linux and Windows. On those platforms, the condor_startd frequently cannot determine console (keyboard or mouse) activity directly from the system, and requires a separate process to do so. On Linux, the condor_kbdd connects to the X Server and periodically checks to see if there has been any activity. On Windows, the condor_kbdd runs as the logged-in user and registers with the system to receive keyboard and mouse events. When it detects console activity, the condor_kbdd sends a command to the startd. That way, the startd knows the machine owner is using the machine again and can perform whatever actions are necessary, given the policy it has been configured to enforce.

condor_ckpt_server

This is the checkpoint server. It services requests to store and retrieve checkpoint files. If your pool is configured to use a checkpoint server but that machine (or the server itself is down) Condor will revert to sending the checkpoint files for a given job back to the submit machine.

condor_quill

This daemon builds and manages a database that represents a copy of the Condor job queue. The condor_q and condor_history tools can then query the database.

condor_dbmsd

This daemon assists the condor_quill daemon.

condor_gridmanager

This daemon handles management and execution of all grid universe jobs. The condor_schedd invokes the condor_gridmanager when there are grid universe jobs in the queue, and the condor_gridmanager exits when there are no more grid universe jobs in the queue.

condor_credd

This daemon runs on Windows platforms to manage password storage in a secure manner.

condor_had

This daemon implements the high availability of a pool's central manager through monitoring the communication of necessary daemons. If the current, functioning, central manager machine stops working, then this daemon ensures that another machine takes its place, and becomes the central manager of the pool.

condor_replication

This daemon assists the condor_had daemon by keeping an updated copy of the pool's state. This state provides a better transition from one machine to the next, in the event that the central manager machine stops working.

condor_transferer

This short lived daemon is invoked by the condor_replication daemon to accomplish the task of transferring a state file before exiting.

condor_procd

This daemon controls and monitors process families within Condor. Its use is optional in general but it must be used if privilege separation (see Section 3.6.14) or group-ID based tracking (see Section 3.13.12) is enabled.

condor_job_router

This daemon transforms vanilla universe jobs into grid universe jobs, such that the transformed jobs are capable of running elsewhere, as appropriate.

condor_lease_manager

This daemon manages leases in a persistent manner. Leases are represented by ClassAds.

condor_rooster

This daemon wakes hibernating machines based upon configuration details.

condor_defrag

This daemon manages draining of machines with fragmented partitionable slots so that they become available for jobs requiring a whole machine or larger fraction of a machine.

condor_shared_port

This daemon listens for incoming TCP packets on behalf of Condor daemons, thereby reducing the number of required ports that must be opened when Condor is accessible through a firewall.

condor_hdfs

This daemon manages the configuration of a Hadoop file system as well as the invocation of a properly configured Hadoop file system.

See figure 3.1 for a graphical representation of the pool architecture.

Figure 3.1: Pool Architecture