How to Monitor Cloud-Based Low-Latency Streams at Scale
What is the essential monitoring checklist for maintaining efficiency, 弹性, and performance for large-scale low-latency streaming using cloud-based workflows? 艾伦·麦克伦南主席， 2G数字后置优化, discusses the key components and tools for an effective live stream monitoring strategy with Ryan Jespersen, 产品策略总监, 杜比.io，在这个剪辑中 流媒体连接.
McLennan asks Jespersen to break down the methods he uses to monitor streams at scale efficiently.
“It's very, very, very hard to scale anything these days,” Jespersen says. “我在。 Wowza 当我们构建Wowza Cloud时. 你见过 Mux 在过去的五年中，他们几乎是凭空冒出来的, six years to become one of the biggest providers in the industry. AWS元素 这个也是吗?. These are all cloud services that are black boxes for people who are using them. You send in an ingest and get an output, but you see nothing happening in the ecosystem. So what's really important when you build any cloud service is giving your customers access to what's happening in this black box.” He emphasizes that customer monitoring tools must include error detection, 参数监测, 以及客户端问题跟踪元素.
Jespersen highlights that he was a part of a startup, Millicast杜比收购了这家公司. “这就是我们的WebRTC流媒体服务. 我们两件事都做. 我们有一个原点和一条边. 我们的边缘, 我们所做的一切都是有状态的, 这意味着它就像过去的RTMP, where it's very hard to scale because you're creating a peer-to-peer connection between an edge server and a client. We offer analytical tools for our customers to see and monitor if there are issues on the distribution side. So, we offer that through an API on the contribution and distribution side in our dashboard. And essentially, it's true of every cloud service.”
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