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

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Publication Date: 3/21/2022

Event: First Workshop on Serverless Computing for Pervasive Cloud-Edge-Device Systems and Services (STARLESS ‘22)

Reference: pp. 699-705, 2022Streaming, serverless, auto-scaling, reinforcement-learning, edge-cloud

Authors: Giuseppe Coviello, NEC Laboratories America, Inc.; Kunal Rao, NEC Laboratories America, Inc.; Biplob Debnath, NEC Laboratories America, Inc.; Oliver Po, NEC Laboratories America, Inc.; Srimat T. Chakradhar, NEC Laboratories America, Inc.

Abstract: 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.

Publication Link: https://ieeexplore.ieee.org/document/9767400