a dynamic critical path algorithm for scheduling scientific workflow applications on global grids

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2008-06- 04 A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids Report: Wei-Cheng Lee e-Science IEEE 2007

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A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids. Report: Wei-Cheng Lee. e-Science IEEE 2007. Abstract. - PowerPoint PPT Presentation

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Page 1: A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids

2008-06-04

A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids

Report: Wei-Cheng Leee-Science IEEE 2007

Page 2: A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids

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Abstract

Dynamic Critical Path (DCP) based workflow scheduling algorithm that determines efficient mapping of tasks by calculating the critical path in the workflow task graph at every step.

It assigns priority to a task in the critical path which is estimated to complete earlier.

Using simulation, we have compared the performance of our proposed approach with other existing heuristic and meta-heuristic based scheduling strategies for different type and size of workflows.

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Outline

The Proposed DCP-G Algorithm2

Performance Evaluation4

Introduction31

Calculation of AEST and ALST in DCP-G33

Results and Observations35

Conclusions4

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Introduction

Dynamic Critical Path (DCP)

Dynamic Critical Path for Grids (DCP-G)

Design for mapping tasks on to homogeneous processors.

Map and schedule tasks in a workflow on to heterogeneous resources in a dynamic Grid environment.

Heuristic

Meta-heuristic

Myopic, Min-Min, Max-Min, HEFT.

GRASP, Genetic Algorithm.

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The Proposed DCP-G Algorithm

For a task graph, the lower and upper bounds of starting time for a task are denoted:

Absolute Earliest Start Time (AEST)

Absolute Latest Start Time (ALST)

DCP algorithm DCP-G• The initial AEST and ALST values are calculated for the resource which provides the minimum execution time for the task.

• For mapping a task on the critical path, all available resources are considered by DCP-G

• When a task is mapped to a resource, its execution time and data transfer time from the parent node are updated accordingly.

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Calculation of AEST and ALST in DCP-G

In DCP-G, the start time of a task is not finalized until it is mapped to a resource.

Absolute Execution Time

Absolute Data Transfer Time

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Here, the communication time between two tasks is considered to be zero if they are mapped to the same resource, and equal to the ADTT of parent task, if the child is not mapped yet.

Dynamic Critical Path Length

ALST of a task t in resource R, denoted as ALST(t, R)

Calculation of AEST and ALST in DCP-G

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Resource Selection

To select the resource that provides the minimum execution time for that task.

This is discovered by checking all the available resources for one that minimizes the potential start time of the critical child task on the same resource, where the critical child task is the one with the least difference of AEST and ALST among all the child tasks of the critical task.

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DCP-G Example

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T0`s AET is: 150000MI / 1500MIPS = 100/PerSecond

T0`s ADTT is: 1GByte / 200Mbps = 8Gb/200Mbps

= 40/PerSecond

T2`s AEST is: 0 + 100 + 40 = 140

T2`s ALST is: 420 – 200 – 80 = 140

DCPL is : 840 + 50 = 890

DCP-G Example

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T2`s AEST is: 0 + 100 + 0 = 100

T2`s ALST is: 380 – 200 – 80 = 100

If T4 was mapped to R2, the DCPL should equal to 770.

Because the AEST(T4) is: 300+400+20=720

So, DCPL is: 720 + 50 = 770.

Whereas, mapping to R1, AEST(T4) is: 300 + 400 + 0 = 700, and DCPL is: 700 + 50 = 750

DCP-G Example

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Performance Evaluation

1. Workflow model.

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Performance Evaluation

2. Resource model.

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Results and Observations

1. Execution time in static environment.

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Results and Observations

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Results and Observations

2. Execution time in dynamic environment.

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Results and Observations

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3. Scheduling time.

GRASP creates Restricted Candidate List (RCL) for each unmapped ready task and then selects resource for the task in random.

Results and Observations

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Conclusion

It is evident that among the heuristics- based scheduling techniques, DCP-G can generate better schedule by up to 20% in static environment, especially for random and parallel workflow, irrespective or workflow size.

GA and GRASP can generate more effective schedule than DCP-G for random and fork-join workflow, but they suffer from the problem of higher scheduling time.

In dynamic environment, heuristics based techniques adapt to the dynamic nature of resources and can avoid performance degradation. But metaheuristics based techniques perform worse in this situation due to the unavailability of mapped resources at certain intervals.

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Wei-Cheng Lee