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:
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
Introduction #1 Parallel programming Share memory Pthread – data dependency Message passing MPI – time delay
Introduction #2 Issues Data dependency Cost of data passing Load balance
Introduction #3 Cloud computation Data segmentation Computing capacity
Introduction #4 GOP-based encoding Independence between GOPs
Introduction #5 Paralleling in GOP-based Thread1 Thread2 Thread3
Related work #1 FCFS - First come first server  Easy to implement Load balancing problem is still exist
Related work #3 MCT – Minimal complete time  Map-Reduce-based
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
Problem formulation and system architecture #2 The overall completion time of set S k is. Goal.
Problem formulation and system architecture #3 Optimal solution. n means n task and m means m cores
Problem formulation and system architecture #4 Flow chart of the proposed method
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.
Problem formulation and system architecture #6 For complexity estimation of video transcoding tasks, the existing algorithms   can be utilized.
Proposed method #1 The framework includes three modules Task pre-analysis Adaptive threshold segmentation Minimal finish time
Proposed method #2 The threshold of segmentation
Proposed method #3
Proposed method #4 The optical finish time The finish time
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.
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
Proposed method #7
Experiment results #1
Experiment results #2
Experiment results #3
Conclusion Load balancing problem is a NP-hard problem. The proposed algorithm has strong robustness to the task launching delay.