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1 Trace-Based Characteristics of Grid Workflows Alexandru Iosup and Dick Epema PDS Group Delft University of Technology The Netherlands Simon Ostermann,

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Presentation on theme: "1 Trace-Based Characteristics of Grid Workflows Alexandru Iosup and Dick Epema PDS Group Delft University of Technology The Netherlands Simon Ostermann,"— Presentation transcript:

1 1 Trace-Based Characteristics of Grid Workflows Alexandru Iosup and Dick Epema PDS Group Delft University of Technology The Netherlands Simon Ostermann, Radu Prodan, and Thomas Fahringer DPS Group University of Innsbruck Austria

2 2 Why are Grid Workflows Interesting? Grids promise reliable and easy-to-use computational infrastructure for e-Science Full automation from experiment design to final result Often, automation = workflows Jobs comprising inter-related computing and data-transfer tasks

3 3 Why are the Characteristics of Grid Workflows Interesting? For focusing on the right research problems What are the interesting characteristics? Number of nodes? Number of edges? Other characteristics… For simulation studies Optimizing a scheduler for one workload does not make it useful for another (often quite the contrary) … optimizing for a workload type is better For performance evaluation in real environments The system tuned to one workload

4 4 Outline Introduction Method for Grid Workflow Analysis Austrian Grid Traces Grid Workflow Characteristics Conclusion and Future Work

5 5 Method for Grid Workflow Analysis [1/3] Overview Goal: establish the main characteristics of grid workflow such that building a workflow-based grid workload model is greatly facilitated Grid workflow characteristics Workflow-intrinsic Environment-related

6 6 Method for Grid Workflow Analysis [2/3] Intrinsic Workflow Characteristics Size and structure of the workflow Number of nodes (N)/edges (E) Branching Factor = N/E Work Size = task runtime of a task on a base platform [SI2k] Work Size Variability = ratio longest vs. shortest WF task Sequential execution path Critical execution path Graph level (L) = length of critical execution path Arrival patterns Daily patterns: Peak Hours Weekly patterns: Week-end vs. Work Days

7 7 Method for Grid Workflow Analysis [3/3] Environment-Related WF Characteristics Time-related Makespan (MS) = time between WF entering and exiting system Scheduler-related Speedup (S) = MS / Sequential Execution Path Size Normalized Schedule Length (NSL) = MS / Critical Path Size Failure-related Success rate = % tasks finished correctly, per WF

8 8 Outline Introduction Method for Grid Workflow Analysis Austrian Grid Traces Grid Workflow Characteristics Conclusion and Future Work

9 9 The Austrian Grid Traces Austrian Grid: 8 sites, ~500 processors Two non-overlapping long-term traces from two workflow engines: Askalon DEE, Askalon EE2 Workflows: mostly testing, but many jobs similar to production workflows Production areas: material sciences, astrophysics, weather prediction, engineering, movie rendering

10 10 Outline Introduction Method for Grid Workflow Analysis Austrian Grid Traces Grid Workflow Characteristics Conclusion and Future Work

11 11 Intrinsic Workflow Characteristics [1/3] Number of nodes 75% WFs have <40 tasks 95% WFs have < 200 tasks 200 tasks 40 tasks

12 12 Intrinsic Workflow Characteristics [2/3] Task Work Size >80% WFs take <2 minutes on 1000-SI2k machine >95% WFs take <10 minutes on 1000-SI2k machine 10 mins 2 mins

13 13 Intrinsic Workflow Characteristics [3/3] Work Size Variability >80% WFs have <10 task size variability But still many WFs with high task size variability 10 200

14 14 Classes of Workflows Simple classifier (experience from previous work) Future: data mining techniques

15 15 Environment-Related Characteristics Workflow class matters: better SU for “easier” classes Large-and-Flat “easier” than Large-and-Branchy Large-and-Branchy “easier” than Branchy (o/head)

16 16 Outline Introduction Method for Grid Workflow Analysis Austrian Grid Traces Grid Workflow Characteristics Conclusion and Future Work

17 17 Conclusion and Future Work Method for the analysis of grid workflows Intrinsic workflow characteristics Environment-dependent workflow characteristics More statistical details than average/std.deviation (Normal is not the typical distribution in computer science) Analysis of two workflow-based traces from Austrian Grid Future work Apply method to more traces Design workflow-based grid workload model

18 18 Thank you! Questions? Remarks? Observations? Help building our community’s Grid Workloads Archive: http://gwa.ewi.tudelft.nl/


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