1. 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez Miquel Angel Senar Emilio Luque Universitat Autònoma de Barcelona Elisa.Heymann@uab.es

2. 2 Our Problem T1 T2 T3 T4 T5 T6 T7 Scheduling Policies Workflow Engines

3. 3 Our Solution T1 T2 T3 T4 T5 T6 T7 Scheduling Policies Workflow Engines SchedFlow

4. 4 Outline ›Introduction ›SchedFlow ›Experimental Study ›Conclusions

5. 5 Introduction ›For executing a workflow on a distributed environment, we need: ›Scheduling policy integrated into a ›Workflow engine ›Reduce makespan ›Factors ›Workload size ›Inaccurate computing and communication times ›Machines appearing/disappering dynamically

6. 6 Introduction ›With SchedFlow, we assessed the influence of the workload on the makespan considering: ›Different scheduling policies ›Different workflow engines

7. SchedFlow T1 T2 T3 T4 T5 T6 T7 User Policy API queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs

8. T1 T2 T3 T4 The user submits a workflow SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs User Policy API

9. T1 T2 T3 The Scheduler uses the specified scheduling policy on the available resources discovered by the Observer. M1 M2 M3 T4 M4 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs

10. T1 T2 T3 The Controller receives the first task-machine pairs M2 M3 T4 M4 M1 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs

11. T1 T2 T3 The Controller tells the adaptor which engine to use. The adaptor deals with formatting and enqueues the task. M2 M3 T4 M4 M1 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs

12. T2 T3 M2 M3 T4 M4 M1 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs T1

13. T2 T3 The Engine sends the task to the assigned machine. The Observer checks the Engine log for finished tasks. M2 M3 T4 M4 SchedFlow M1 T1 queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs

14. T2 T3 When the task finishes, the Observer notifies the Scheduler. M2 M3 T4 M4 SchedFlow M1 queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs

15. T2 T3 T4 M4 The Scheduler finds the tasks that have their dependencies satisfied and sends them to the Controller. SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3

16. T2 T3 T4 M4 M2 M3 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs

17. T4 M4 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3 T2 T3

18. T4 M4 M2 M3 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3 T2 T3

19. T2 finishes OK. M3 is claimed. T4 M4 M2 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3 T3

20. The Observer detects the problem and T3 is removed from M3 and dynamcally rescheduled. T4 M4 M2 M3 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3 T3

21. T3 is rescheduled. The Observer does not include M3 as an available resource. T4 M4 T3 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3

22. T4 M4 T3 M2 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3

23. T4 M4 T3 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3

24. T4 M4 T3 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3

25. T4 M4 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3 T3

26. T4 M4 SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3 T3

27. T4 M4 T3 finishes OK. The Observer notifies the Scheduler, and it releases T4. SchedFlow queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs M2 M3

28. T4 SchedFlow M4 queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs

29. T4 SchedFlow M4 queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs

30. SchedFlow M4 queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs T4

31. SchedFlow M4 queue Task manager ControllerObserver Scheduler Adaptor Scheduler Adaptor Workflow Engine logs T4 When T4 finishes the Observer computes the makespan.

32. 32 Experimental Study ›Execution environment: –140 machines ›Workflow applications: –Montage (53 tasks) –LIGO (81 tasks) ›Workflow engines: –Condor-DAGMan 7.0 –Taverna 1.4.8 –Karajan 4_0_a1

33. 33 Experimental Study ›Scheduling policies: –Default –Min-min –HEFT –BMCT

34. 34 Experimental Study ›Input workload: –400 MB –1024 MB ›We studied the effect of the scheduling policies. ›We measured application makespan ›Real executions

35. 35 Results ›Mantage ran on Taverna, DAGMan, Karajan ›400 MB input workload ›120 executions ›Default scheduling policy

36. 36 Results ›Same experiments but using SchedFlow ›Min-min, HEFT, BMCT ›Rescheduling

37. 37 Results ›Mantage ran on Taverna, DAGMan, Karajan ›1024 MB input workload ›120 executions ›Default scheduling policy

38. 38 Results ›Same experiments but using SchedFlow ›Min-min, HEFT, BMCT ›Rescheduling

39. 39 Results ›LIGO ran on Taverna, DAGMan, Karajan ›400 MB input workload ›120 executions ›Default scheduling policy

40. 40 Results ›Same experiments but using SchedFlow ›Min-min, HEFT, BMCT ›Rescheduling

41. 41 Results ›LIGO ran on Taverna, DAGMan, Karajan ›1024 MB input workload ›120 executions ›Default scheduling policy

42. 42 Results ›Same experiments but using SchedFlow ›Min-min, HEFT, BMCT ›Rescheduling

43. 43 Conclusions ›No single scheduling policy is the best for all scenarios ›SchedFlow allows us to obtain better performance providing: –Flexibility regarding scheduling policies –Support for rescheduling –Integration with Workflow Engines

44. 44 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez Miquel Angel Senar Emilio Luque Universitat Autònoma de Barcelona Elisa.Heymann@uab.es