1 / 20

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

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

alpha
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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.

  3. 3 3 3 1 3 5 Outline Introduction 2 The Proposed DCP-G Algorithm Calculation of AEST and ALST in DCP-G 4 Performance Evaluation Results and Observations 4 Conclusions

  4. Introduction Dynamic Critical Path (DCP) Design for mapping tasks on to homogeneous processors. Dynamic Critical Path for Grids (DCP-G) Map and schedule tasks in a workflow on to heterogeneous resources in a dynamic Grid environment. Heuristic Myopic, Min-Min, Max-Min, HEFT. Meta-heuristic GRASP, Genetic Algorithm.

  5. DCP algorithm DCP-G 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) • 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.

  6. 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

  7. Calculation of AEST and ALST in DCP-G 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)

  8. 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.

  9. DCP-G Example

  10. DCP-G Example 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

  11. DCP-G Example 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

  12. Performance Evaluation 1. Workflow model.

  13. Performance Evaluation 2. Resource model.

  14. Results and Observations 1. Execution time in static environment.

  15. Results and Observations

  16. Results and Observations 2. Execution time in dynamic environment.

  17. Results and Observations

  18. Results and Observations 3. Scheduling time. GRASP creates Restricted Candidate List (RCL) for each unmapped ready task and then selects resource for the task in random.

  19. 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.

  20. Thank You Wei-Cheng Lee For Your Attention!

More Related