EdgeServe: An Execution Layer for Decentralized Prediction

Autor:	Shaowang, Ted, Krishnan, Sanjay
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Databases Computer Science - Distributed Parallel and Cluster Computing Databases (cs.DB) Distributed Parallel and Cluster Computing (cs.DC) Machine Learning (cs.LG)
Popis:	The relevant features for a machine learning task may be aggregated from data sources collected on different nodes in a network. This problem, which we call decentralized prediction, creates a number of interesting systems challenges in managing data routing, placing computation, and time-synchronization. This paper presents EdgeServe, a machine learning system that can serve decentralized predictions. EdgeServe relies on a low-latency message broker to route data through a network to nodes that can serve predictions. EdgeServe relies on a series of novel optimizations that can tradeoff computation, communication, and accuracy. We evaluate EdgeServe on three decentralized prediction tasks: (1) multi-camera object tracking, (2) network intrusion detection, and (3) human activity recognition. 13 pages, 8 figures
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcd30c8bdcb75d92c817bd5fc5019970 http://arxiv.org/abs/2303.08028 Zobrazit plný text záznamu