From Data to Actions and Insights at Conviva

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Video streaming is still a challenge, especially with increasing demand for high-quality streaming experiences. Problems can happen anywhere in the complex streaming ecosystem. At Convivia, they collect data about video streaming quality to give their customers (publishers and ecosystem partners) visibility into the end-user experience they’re delivering. Conviva’s job is to distill these data into actionable insights or, better yet, to take automatic actions to improve quality.
In this session, they will discuss two systems they have built to this end:
– AutoAlert is a system for detecting and diagnosing anomalies in streaming quality in real time. Conviva uses Apache Spark to group millions of sessions according to a variety of criteria (e.g. the title of the streaming media, or the device the user is streaming from), and detect anomalies in several quality metrics over time. The detection task runs once per minute, with a detection latency of about 2 minutes.
– GO is a system for directing users to the CDN that will provide the best quality. The architecture for this prediction task mirrors AutoAlert’s architecture.

Our talk will focus on the outcomes we’ve achieved for our customers with these systems.

Session hashtag: #SFent6

About Yan Li

Yan is an Engineering Manager at Conviva, where she has been working on various projects in the area of Internet video streaming, and has built several products used by content publishers and ecosystem partners to monitor and optimize the experience of millions of end viewers. Yan holds a Ph.D. degree in Computer Science from the University of Connecticut.

About Rui Zhang

Rui Zhang is a senior software engineer at Conviva. He works on several Spark applications including:Auto Alert, which detects anomaly in video streaming quality and diagnose root cause in real time; Precision API, which suggests CDN (host of video) with best streaming quality to viewers to improve viewing experience; Golden Standard, which computes the theoretical upper bound of streaming quality given bandwidth and optimization target. Rui Zhang holds a MS in Stanford EE.