An Architecture for Distributed High Performance Video Processing in the Cloud

R. Pereira, M. Azambuja, K. Breitman, M. Endler
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引用次数: 108

Abstract

Video processing applications are notably data intense, time, and resource consuming. Upfront infrastructure investment is usually high, specially when dealing with applications where time-to- market is a crucial requirement, e.g., breaking news and journalism. Such infrastructures are often inefficient, because due to demand variations, resources may end up idle a good portion of the time. In this paper, we propose the Split&Merge architecture for high performance video processing, a generalization of the MapReduce paradigm that rationalizes the use of resources by exploring on demand computing. To illustrate the approach, we discuss an implementation of the Split&Merge architecture, that reduces video encoding times to fixed duration, independently of the input size of the video file, by using dynamic resource provisioning in the Cloud.
云中的分布式高性能视频处理体系结构
视频处理应用程序是一个数据密集、时间和资源消耗非常大的应用程序。前期基础设施投资通常很高,特别是在处理对上市时间有重要要求的应用程序时,例如突发新闻和新闻。这种基础设施通常效率低下,因为由于需求的变化,资源可能会在很长一段时间内处于闲置状态。在本文中,我们提出了用于高性能视频处理的Split&Merge架构,这是MapReduce范式的一种推广,通过探索按需计算来合理化资源使用。为了说明这种方法,我们讨论了Split&Merge架构的实现,通过使用云中的动态资源配置,将视频编码时间减少到固定的持续时间,而不依赖于视频文件的输入大小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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