| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === Cloud multimedia related applications can be found by many in recent years. The multimedia cloud has to serve users under different network environments, so that it has to perform scalable video coding or video transcoding to provide bandwidth compatible video bitstreams. The MPEG-DASH (Dynamic Adaptive Streaming over HTTP) framework has been developed to serve multimedia for users with heterogeneous network and devices. As video processing and transcoding are high time complexity procedures, the cloud platform has to perform task scheduling efficiency to provide prompt reply for user requests. In this research, we design video transcoding methods for cloud computer clusters based on the Yarn concurrent MapReduce processing framework. The Hadoop Distributed File System (HDFS) is also utilized in handling video segment storage and management. The Yarn Hadoop framework is adopted for cloud transcoding operations. We proposed a Label-Based and Complexity Aware Scheduler by Neural Network Model, whose control targets are (1) improve the resource utilization rate; and (2) maintaining high load balancing operation. For the first target, the Complexity-Aware Scheduler is designed to assign tasks with the ascending order of task complexity. It can effectively reduce the convoy effect and improve resource utilization rate. By estimate the task complexity through Neural Network Models to re-order task assignment, the resource utilization rates can be further improved. For the second target, the system monitor worker CPU utilization condition through heartbeat mechanism and adaptively adjusts the number of worker slot certificates to maintain high load balancing operations. Both control targets help to improve the video transcoding performance and yield a shorter processing time. Experiments showed that the proposed scheduling method maintains the CPU and resource utilization rates above 90% and 98%, respectively. The total processing time can be shortened to 50% smaller.
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