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Parallelizing Video Transcoding With Load Balancing On Cloud Computing

Parallelizing Video Transcoding With Load Balancing On Cloud Computing. Song Lin, Xinfeng Zhang, Qin Y, Siwei Ma Circuits and Systems, 2013 IEEE. Outline. Introduction Related work Problem formulation and system architecture Proposed method Experiment Results Conclusion.

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Parallelizing Video Transcoding With Load Balancing On Cloud Computing

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  1. Parallelizing Video Transcoding With Load Balancing On Cloud Computing Song Lin, Xinfeng Zhang, Qin Y, Siwei Ma Circuits and Systems,2013 IEEE

  2. Outline • Introduction • Related work • Problem formulation and system architecture • Proposed method • Experiment Results • Conclusion

  3. Introduction #1 • Parallel programming • Share memory • Pthread – data dependency • Message passing • MPI – time delay

  4. Introduction #2 • Issues • Data dependency • Cost of data passing • Load balance

  5. Introduction #3 • Cloud computation • Data segmentation • Computing capacity

  6. Introduction #4 • GOP-based encoding • Independence between GOPs ...........

  7. Introduction #5 • Paralleling in GOP-based Thread1 Thread2 Thread3

  8. Related work #1 • FCFS - First come first server [2] • Easy to implement • Load balancing problem is still exist

  9. Related work #3 • MCT – Minimal complete time [6] • Map-Reduce-based

  10. Problem formulation and system architecture #1 • Load balance problem on cloud computing • Executing time • Delay time • Data passing • C is complexity and P is computing capacity

  11. Problem formulation and system architecture #2 • The overall completion time of set Sk is • . • Goal • .

  12. Problem formulation and system architecture #3 • Optimal solution • . • n means n task and m means m cores

  13. Problem formulation and system architecture #4 • Flow chart of the proposed method

  14. Problem formulation and system architecture #5 • For video coding, if the input sequence has instantaneous decoder refresh (IDR) frame, this video coding task can be divided into sub-tasks.[7]

  15. Problem formulation and system architecture #6 • For complexity estimation of video transcoding tasks, the existing algorithms [8] [9] can be utilized.

  16. Proposed method #1 • The framework includes three modules • Task pre-analysis • Adaptive threshold segmentation • Minimal finish time

  17. Proposed method #2 • The threshold of segmentation

  18. Proposed method #3

  19. Proposed method #4 • The optical finish time • The finish time

  20. Proposed method #5 • Assign all the tasks sequentially in descending complexity order • For each unassigned task j, the cores are judged in their descending computing capacity order according to the following criterion: assuming the task j is assigned to core k, if Τκ ≤ Tthr, the assignment is verified. Otherwise, we will judge the next core.

  21. Proposed method #6 • If all the cores are traversed and all the computing time are beyond Tthr, the task j will be assigned by MCT algorithm. and Tthr is updated to be the new finish time of the received core Tk

  22. Proposed method #7

  23. Experiment results #1

  24. Experiment results #2

  25. Experiment results #3

  26. Conclusion • Load balancing problem is a NP-hard problem. • The proposed algorithm has strong robustness to the task launching delay.

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