Presentation 15 condor-v1
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Condor is a distributed job scheduling system that was developed at the University of Wisconsin. It uses a publish-subscribe model where worker nodes and jobs describe their attributes and requirements, and Condor matches them. Condor allows checkpointing of jobs so they can be migrated, uses remote system calls to redirect I/O, and supports different universes for serial, parallel, and workflow-based jobs. The Condor architecture consists of daemons running on submit and execute nodes that communicate with each other and central managers.
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Presentation 15 condor-v1
- 1. 15 Condor – A Distributed Job Scheduler Todd Tannenbaum, Derek Wright, Karen Miller, and Miron Livny Beowulf Cluster Computing with Linux, Thomas Sterling, editor, Oct. 2001. Summarized by Simon Kim
- 2. Contents • Introduction to Condor • Using Condor • Condor Architecture • Installing Condor under Linux • Configuring Condor • Administration Tools • Cluster Setup Scenarios
- 3. Introduction to Condor • Distributed Job Scheduler • Condor Research Project at University of Wisconsin-Madison Department of Computer Sciences • Changed name to HTCondor in 2012 – http://research.cs.wisc.edu/htcondor
- 4. Introduction to Condor Condor Job Run Run Idle Monitor Progress Report Queue Run Idle Run Nodes User Policy Complete!
- 5. Introduction to Condor • Workload Management System • Job Queuing Mechanism • Scheduling Policy • Priority Scheme • Resource Monitoring and Management
- 6. Condor Features • Distributed Submission • User/Job Priorities • Job Dependency - DAG • Multiple Job Models – Serial/Parallel Jobs • ClassAds – Job : Machine Matchmaking • Job Checkpoint and Migration • Remote System Calls – Seamless I/O Redirection • Grid Computing – Interaction with Globus Resources
- 7. ClassAds and Matchmaking • Job ClassAd – Looking for Machine – Requirements: Intel, Linux, Disk Space, … – Rank: Memory, Kflops, … • Machine ClassAd – Looking for Job – Requirements – Rank
- 8. Using Condor • Roadmap to Using Condor • Submitting a Job • User Commands • Universes • Standard Universe – Process Checkpointing – Remote System Calls – Relinking – Limitations • Data File Access • DAGMan Scheduler
- 9. Using Condor Batch Job STDIN STDOUT STDERR univers = vanilla executable = foo log = foo.log input = input.data output = output.data queue Submit Description Standard Vanilla PVM MPI Grid Scheduler Universes: Runtime Environment Prepare a Job Submit Serial Job Parallel Job Meta Scheduler $ condor_submit
- 10. Status of Submitted Jobs • $ condor_status -submitters
- 11. All jobs in the Queue • $ condor_q • Removing Job – $ condor_rm 350.0 • Changing Job Priority: -20 ~ 20(high), default: 0 – $ condor_prio –p -15 350.1
- 12. Universes • Execution Environment - Universe • Vanilla – Serial Jobs – Binary Executable and Scripts • MPI Universe – MPI Programs – Parallel Jobs – Only on Dedicated Resources # Submit Description Universe = mpi … machine_count = 8 queue
- 13. Universes • PVM Universe – Master-worker Style Parallel Programs • Written for Parallel Virtual Machine Interface – Both Dedicated and Non- dedicated (workstations) – Condor Acts as Resource Manager for PVM Daemon – Dynamic Node Allocation PVM Daemon Condor # Submit Description Universe = pvm … machine_count = 1..75 queue pvm_addhosts()
- 14. Universes • Scheduler Universe – Meta-Scheduler – DAGMan Scheduler • Complex Interdependencies Between Jobs A B C D * B and C are executed in parallel Job Sequence: A -> B and C -> D
- 15. Universes • Standard Universe – Serial Job – Process Checkpoint, Restart, and Migration – Remote System Calls
- 16. Process Checkpointing • Checkpoint – Snapshot of the Program’s Current State – Preemptive Resume Scheduling – Periodic Checkpoints – Fault Tolerance – No Program Source Code Change • Relinking with Condor System Call Library – Signal Handler • Process State Written to a Local/Network File • Stack/Data Segments, CPU state, Open Files, Signal Handlers and Pending Signals – Optional Checkpoint Server • Checkpoint Repository
- 17. Remote System Calls • Redirects File I/O – Open(), read(), write() -> Network Socket I/O – Sent to ‘condor_shadow’ process on Submit Machine • Handles Actual File I/O • Note that Job Runs on Remote Machine • Relinking Condor Remote System Call Library – $ condor_compile cc myprog.o –o myprog
- 18. Standard Universe Limitations • No Multi-Process Jobs – fork(), exec(), system() • No IPC – Pipes, Semaphores, and Shared Memory • Brief Network Communication – Long Connection -> Delay Checkpoints and Migration • No Kernel-level Threads – User-level Threads Are Allowed • File Access: Read-only or Write-only – Read-Write: Hard to Roll Back to Old Checkpoint • On Linux, Must be Statically Linked
- 19. Data Access from a Job • Remote System Call – Standard Universe • Shared Network File System • What About Non-dedicated Machines (Desktops) ? – Condor File Transfer – Before Run, Input Files Transferred to Remote – On Completion, Output Files Transferred Back to Submit Machine – Requested in Submit Description File • transfer_input_files = <…>, transfer_output_files=<…> • transfer_files=<ONEXIT | ALWAYS | NEVER>
- 20. Condor Architecture Central Manager Machine Negotiator Collector Startd Sched Startd Sched Machine 1 Startd Sched Machine 2 Startd Sched Machine N
- 21. Condor Architecture Central Manager Machine Negotiator Collector Startd Sched Startd Sched Machine 1: Submit Startd Sched Machine N: Execute Starter Job Shadow Condor Remote System Call
- 22. Cluster Setup Scenarios • Uniformed Owned Dedicated Cluster – MPI Jobs on Dedicated Nodes • Cluster of Multi-Processor Nodes – 1VM per Processor • Cluster of Distributively Owned Nodes – Jobs from Owner Preferred • Desktop Submission to Cluster – Submit-only Node Setup • Non-Dedicated Computing Resources – Opportunistic Scheduling and Matchmaking with Process Checkpointing, Migration, Suspend and Resume
- 23. Conclusion • Distinct Features – Matchmaking with Job and Machine ClassAds – Preemptive Scheduling and Migration with Checkpointing – Condor Remote System Call • Powerful Tool for Distributed Scheduling Jobs – Within and Beyond Beowulf Clusters • Unique Combination of Dedicated and Opportunistic Scheduling