Streaming refers to the continuous delivery of digital media content, such as audio or video, over the internet in a real-time or near-real-time manner. Unlike traditional methods of downloading content before playback, streaming allows users to consume media almost instantly without the need for complete file downloads. This is achieved by sending and receiving data in a continuous flow, allowing users to access and enjoy the content as it is delivered.

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DataXe: A System for Application Self-optimization in Serverless Edge Computing Environments

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A key barrier to building performant, remotely managed and self-optimizing multi-sensor, distributed stream processing edge applications is high programming complexity. We recently proposed DataX [1], a novel platform that improves programmer productivity by enabling easy exchange, transformations, and fusion of data streams on virtualized edge computing infrastructure. This paper extends DataX to include (a) serverless computing that automatically scales stateful and stateless analytics units (AUs) on virtualized edge environments, (b) novel communication mechanisms that efficiently communicate data among analytics units, and (c) new techniques to promote automatic reuse and sharing of analytics processing across multiple applications in a lights out, serverless computing environment. Synthesizing these capabilities into a single platform has been substantially more transformative than any available stream processing system for the edge. We refer to this enhanced and efficient version of DataX as DataXe. To the best of our knowledge, this is the first serverless system for stream processing. For a real-world video analytics application, we observed that the performance of the DataXe implementation of the analytics application is about 3X faster than a standalone implementation of the analytics application with custom, handcrafted communication, multiprocessing and allocation of edge resources.

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