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1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin [email protected] Elisa Heyman Gustavo Martínez Miquel Angel Senar Emilio Luque Universitat Autònoma de Barcelona [email protected]

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Page 1: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

1

Using SchedFlow for Performance Evaluation of Workflow Applications

Barton P. Miller

University of Wisconsin

[email protected]

Elisa HeymanGustavo Martínez

Miquel Angel Senar Emilio Luque

Universitat Autònoma de Barcelona

[email protected]

Page 2: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

2

Our Problem

T1

T2 T3

T4 T5 T6

T7

Scheduling Policies

Workflow Engines

Page 3: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

3

Our Solution

T1

T2 T3

T4 T5 T6

T7

Scheduling Policies

Workflow Engines

SchedFlow

Page 4: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

4

Outline› Introduction› SchedFlow› Experimental Study› Conclusions

Page 5: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

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

Page 6: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

6

Introduction

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

Page 7: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

SchedFlowT1

T2 T3

T4 T5 T6

T7

User PolicyAPI

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

Page 8: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T1

T2 T3

T4

The user submits a workflow

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

User PolicyAPI

Page 9: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

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

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

Page 10: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T1

T2

T3

The Controller receives the first task-machine pairs

M2

M3

T4 M4

M1

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

Page 11: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

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

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

Page 12: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T2

T3

M2

M3

T4 M4

M1

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logsT1

Page 13: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

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

M1T1

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

Page 14: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T2

T3

When the task finishes, the Observer notifies the Scheduler.

M2

M3

T4 M4

SchedFlow

M1

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

Page 15: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T2 T3

T4 M4

The Scheduler finds the tasks that have their dependencies satisfied and sends them to the Controller.

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

Page 16: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T2 T3

T4 M4

M2

M3

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

Page 17: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T4 M4

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

T2 T3

Page 18: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T4 M4

M2

M3

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

T2

T3

Page 19: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T2 finishes OK.M3 is claimed.

T4 M4

M2

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

T3

Page 20: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

The Observer detects the problem and T3 is removed from M3 and dynamcally rescheduled.

T4 M4

M2

M3

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3T3

Page 21: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T3 is rescheduled. The Observer does not include M3 as an available resource.

T4 M4

T3

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

Page 22: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T4 M4

T3 M2

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

Page 23: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T4 M4

T3

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

Page 24: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T4 M4

T3

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

Page 25: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T4 M4

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

T3

Page 26: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T4 M4

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

T3

Page 27: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T4 M4

T3 finishes OK. The Observer notifies the Scheduler, and it releases T4.

SchedFlow

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

M2

M3

Page 28: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T4

SchedFlow

M4

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

Page 29: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

T4

SchedFlow

M4

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

Page 30: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

SchedFlow

M4

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logsT4

Page 31: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

SchedFlow

M4

queue

Task manager

Controller Observer

SchedulerScheduler

SchedulerScheduler

Adaptor

Scheduler

Adaptor

Workflow Engine

logs

T4

When T4 finishes the Observer computes the makespan.

Page 32: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

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

Page 33: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

33

Experimental Study

› Scheduling policies:– Default– Min-min– HEFT– BMCT

Page 34: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

34

Experimental Study

› Input workload:– 400 MB– 1024 MB

› We studied the effect of the scheduling policies.

› We measured application makespan› Real executions

Page 35: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

35

Results› Mantage ran on Taverna, DAGMan,

Karajan› 400 MB input workload› 120 executions› Default scheduling policy

Taverna DAGMan Karajan

0

2000

4000

6000

8000

10000

12000

14000

Worflow engine with default Scheduling Policies

Ma

ke

sp

an

av

era

ge

(s

ec

.)

Page 36: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

36

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

Taverna DAGMan Karajan

0

2000

4000

6000

8000

10000

12000

14000

Default min-min HEFT BMCT

Worflow engine with differents Scheduling Policies

Ma

ke

sp

an

av

era

ge

(s

ec

.)

Page 37: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

37

Results› Mantage ran on Taverna, DAGMan,

Karajan› 1024 MB input workload› 120 executions› Default scheduling policy

Taverna DAGMan Karajan

0

4000

8000

12000

16000

20000

24000

28000

Worflow engine with default Scheduling Policies

Ma

ke

sp

an

av

era

ge

(s

ec

.)

Page 38: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

38

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

Taverna DAGMan Karajan

0

4000

8000

12000

16000

20000

24000

28000

Default min-min HEFT BMCT

Worflow engine with differents Scheduling Policies

Ma

ke

sp

an

av

era

ge

(s

ec

.)

Page 39: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

39

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

Taverna DAGMan Karajan

0

4000

8000

12000

16000

20000

24000

28000

Worflow engine with default Scheduling Policies

Ma

ke

sp

an

av

era

ge

(s

ec

.)

Page 40: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

40

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

Taverna DAGMan Karajan

0

4000

8000

12000

16000

20000

24000

28000

Default min-min HEFT BMCT

Worflow engine with differents Scheduling Policies

Ma

ke

sp

an

av

era

ge

(s

ec

.)

Page 41: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

41

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

Taverna DAGMan Karajan

0

10000

20000

30000

40000

50000

60000

Worflow engine with default Scheduling Policies

Ma

ke

sp

an

av

era

ge

(s

ec

.)

Page 42: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

42

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

Taverna DAGMan Karajan

0

10000

20000

30000

40000

50000

60000

Default min-min HEFT BMCT

Worflow engine with differents Scheduling Policies

Ma

ke

sp

an

av

era

ge

(s

ec

.)

Page 43: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

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

Page 44: 1 Using SchedFlow for Performance Evaluation of Workflow Applications Barton P. Miller University of Wisconsin bart@cs.wisc.edu Elisa Heyman Gustavo Martínez

44

Using SchedFlow for Performance Evaluation of Workflow Applications

Barton P. Miller

University of Wisconsin

[email protected]

Elisa HeymanGustavo Martínez

Miquel Angel Senar Emilio Luque

Universitat Autònoma de Barcelona

[email protected]


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