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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.