1. Condor and the Grid D. Thain, T. Tannenbaum, M. Livny Christopher M. Moretti 23 February 2007

2. 2 Problem & Opportunity  Users need CPUs Scientific computing Mathematical modeling Data mining  Many CPU cycles are unused Personal workstations General use laboratories Research machines

3. 3 Solution: Condor  “A hunter of idle workstations” Keeps track of resources  needed and available Determines and assigns matches Monitors progress Cleans up and reports results

4. 4 Architecture  Three principals: Agent: machine needing resources Matchmaker Resource: machine lending resources  Three phases: Advertising Matching/Claiming Deploying/Executing

5. 5 Advertising MatchMaker AgentLender I need X I have Y idle.cse.nd.eduneedy.cse.nd.edu Does Y satisfy X?

6. 6 Matching & Claiming MatchMaker AgentLender Use idle.cse.nd.edu Listen for needy.cse.nd.edu idle.cse.nd.eduneedy.cse.nd.edu Are you still available? Yes.

7. 7 Deploying / Executing AgentLender idle.cse.nd.eduneedy.cse.nd.edu Shadow Fork! Run job J. J I need file /tmp/foo. Sandbox Split Execution

8. 8 Matching  How are matches determined? Policy ClassAds  Why independently claim a match?  What if the Matchmaker dies?

9. 9 ClassAds  MyType=“Job”  TargetType=“Machine”  Requirements= (( other.Arch==“INTEL” && other.OpSys==“LINUX” && KeyboardIdle>600 ))  Cmd=“/tmp/a.out”  Owner=“cmoretti”  MyType=“Machine”  TargetType=“Job”  Machine= “dustpuppy.cse.nd.edu”  Requirements= (( KeyboardIdle>600 ))  Arch=“INTEL”  OpSys=“LINUX”

10. 10 Flocking  Using another pool’s resources Utilize more total resources Find resources that match needs  Two methods Gateway flocking Direct flocking

11. 11 Gateway Flocking  Each pool has a known “gateway”  Gateways negotiate sharing Advertise resources and needs Transmit requests to local matchmaker  Pool-level granularity Accounting Policy  Now obsolete

12. 12 Gateway Flocking Gateway MM R R R R R R R A 2 3 5 4 5 1

13. 13 Direct Flocking  Agents report to other matchmakers No gateways Equivalent to being in multiple pools?  Now the preferred (only) method

14. 14 Gateway Flocking MM R R R R R R R A 2 3 1

15. 15 Flocking Comparison + Transparency + Fosters organization-level sharing - Poor accounting - Complicated + No gateways + Individual relationships supported - Non-transparent - Fewer organization-level agreements Gateway FlockingDirect Flocking

16. 16 Things Aren’t Perfect  What happens if (when) … Matchmaker goes down Network or Agent fails during deploy Resource or App fails during compute  Non-dedicated machines. How do we keep owners happy? What happens when an owner reclaims a resource?

17. 17 Total Consumption in 2006 CPU-Hours Harnessed by Condor(48%)1161176 CPU-Hours Totally Unused(39%)934277 CPU-Hours Consumed by Owner at Keyboard(11%)281003 CPU-Hours Total(100%)2376456 http://www.cse.nd.edu/~ccl/operations/condor/2005/users.html Condor at Notre Dame Harnessing Idle Computers with Condor at Notre Dame: Impact on Research in 2006 “Harnessing Idle Computers with Condor at Notre Dame: Impact on Research in 2006”, Douglas Thain

18. 18 Current Donors Feb 2007 OwnerNodesCPUsStorage (TB) CRC/OIT92 3.7 CSE7312411.7 Prof. Thain59915.5 Prof. Flynn18350.65 Prof. Striegel 10200.65 Misc717 Total25937920.2 TB Harnessing Idle Computers with Condor at Notre Dame: Impact on Research in 2006 “Harnessing Idle Computers with Condor at Notre Dame: Impact on Research in 2006”, Douglas Thain

19. 19 CPU History Harnessing Idle Computers with Condor at Notre Dame: Impact on Research in 2006 “Harnessing Idle Computers with Condor at Notre Dame: Impact on Research in 2006”, Douglas Thain

20. 20 Recap  Condor facilitates distributed computation on dedicated or scavenged CPUs arranged by a matchmaker using ClassAds.  Split Execution is necessary to fit the job’s needs to the environment.  An agent can advertise to multiple matchmakers to examine more potential matches.