MPEG NBMP testbed for evaluation of real-time distributed media processing workflows at scale

Roberto Ramos-Chavez, R. Mekuria, Theodoros Karagkioules, Dirk Griffioen, Arjen Wagenaar, Mark Ogle
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引用次数: 2

Abstract

Real-time Distributed Media Processing Workflows (DMPW) are popular for online media delivery. Combining distributed media sources and processing can reduce storage costs and increase flexibility. However, high request rates may result in unacceptable latency or even failures in incorrect configurations. Thus, testing DMPW deployments at scale is key, particularly for real-time cases. We propose the new MPEG Network Based Media Processing (NBMP) standard for this and present a testbed implementation that includes all the reference components. In addition, the testbed includes a set of configurable functions for load generation, monitoring, data-collection and visualization. The testbed is used to test Dynamic Adaptive HTTP streaming functions under different workloads in a standardized and reproducible manner. A total of 327 tests with different loads and Real-Time DMPW configurations were completed. The results provide insights in the performance, reliability and time-consistency of each configuration. Based on these tests, we selected the preferred cloud instance type, considering hypervisor options and different function implementation configurations. Further, we analyzed different processing tasks and options for distributed deployments on edge and centralized clouds. Last, a classifier was developed to detect if failures happen under a certain workload. Results also show that, normalized inter-experiment standard deviation of the metric means can be an indicator for unstable or incorrect configurations.
用于大规模评估实时分布式媒体处理工作流的MPEG NBMP测试平台
实时分布式媒体处理工作流(DMPW)在在线媒体交付中非常流行。将分布式媒体源和处理相结合可以降低存储成本并增加灵活性。但是,高请求率可能导致不可接受的延迟,甚至错误配置导致的故障。因此,大规模测试DMPW部署是关键,特别是在实时情况下。为此,我们提出了新的MPEG基于网络的媒体处理(NBMP)标准,并给出了一个包含所有参考组件的测试平台实现。此外,该测试平台还包括一套可配置的功能,用于负载生成、监控、数据收集和可视化。该测试平台用于以标准化和可重现的方式测试不同工作负载下的动态自适应HTTP流功能。在不同负载和实时DMPW配置下,共完成了327次试验。结果提供了对每个配置的性能、可靠性和时间一致性的见解。基于这些测试,我们选择了首选的云实例类型,同时考虑了管理程序选项和不同的功能实现配置。此外,我们还分析了边缘云和集中式云中分布式部署的不同处理任务和选项。最后,开发了一个分类器来检测在一定的工作负荷下是否发生故障。结果还表明,归一化的实验间标准偏差度量均值可以作为不稳定或不正确配置的指标。
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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