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

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Presentation on theme: "Parallelizing Video Transcoding With Load Balancing On Cloud Computing Song Lin, Xinfeng Zhang, Qin Y, Siwei Ma Circuits and Systems, 2013 IEEE."— Presentation transcript:

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 S k 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 Τ κ T thr, 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 T thr, the task j will be assigned by MCT algorithm. and T thr is updated to be the new finish time of the received core T k

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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