For a multimedia cloud computing platform, it needs to perform videotranscoding to provide bandwidth-compatible bit-streams for users. In configuring a MapReduce system, it allocates slots to workers with the assumpt...
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For a multimedia cloud computing platform, it needs to perform videotranscoding to provide bandwidth-compatible bit-streams for users. In configuring a MapReduce system, it allocates slots to workers with the assumption of homogeneous worker power and performs task scheduling by assuming equal task time complexity. However, the computing power of a practical cluster of workers and the time complexity of tasks is time-varying in any case. The task-scheduling algorithm has to well manipulate these heterogeneous cloud resources and task complexity. We first find a good partition size for a videotranscoding job for efficient processing. We proposed a Complexity-Aware Scheduling (CAS) algorithm that reorders task assignment priorities according to task complexity to maintain load-balancing operations. By utilizing a Neural Network model to refine the task complexity estimation for the CAS(CASNN), the scheduling and transcoding speedup performances can further be improved. Based on the CASNN, we proposed a Dynamically Adjusting Slot number allocation (DAS) method, DASCASNN, to adjust the slots according to resource utilization status to improve the processing performance. Experimental results show the proposed DASCASNN can help reduce 30% of the transcoding time on average as compared to available schedulers and increase the resource utilization rates from 82.7% to 98%.
cloudvideo processing and streaming services has to be delivered under heterogeneous network and device environments. Scalable video coding and transcoding are required to serve heterogeneous users. As the task sched...
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ISBN:
(纸本)9781509015528
cloudvideo processing and streaming services has to be delivered under heterogeneous network and device environments. Scalable video coding and transcoding are required to serve heterogeneous users. As the task scheduling algorithm pre-configures a Hadoop MapReduce platform with the assumption of homogeneous node processing capability and task complexity, it cannot well accommodate the practical heterogeneous resources and tasks. In this research, we proposed a Dynamic Adjustment Slot and Complexity Aware Scheduler (DASCAS) algorithm to assign tasks under heterogeneous resources and tasks environments. Complexities of decomposed video segments are evaluated for setting task priority. The scheduling algorithm utilizes a speculative mechanism to detect potential late tasks to re-assign to other nodes for fast processing. It also monitors processing status of the distributed computer cluster and dynamically adjust the number of slots for load balance operations. Experiments show that the proposed method can reduce the transcoding time to 14%similar to 24% smaller and improve the resource utilization rates to 2%similar to 12% higher.
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