2018/04/14 by Jianxin Zhao, Zhao, Jianxin, Tudor Tiplea +7 · 2 citations
Computer Science · Engineering · #Analytics #Artificial intelligence #Business #Cloud Computing and Resource Management #Cloud computing #Computer network #Computer science #Container (type theory) #Data analysis #Data mining #Data science #Databases (cs.DB) #Edge device #Engineering #Enhanced Data Rates for GSM Evolution #FOS: Computer and information sciences #IoT and Edge/Fog Computing #JavaScript #Operating system #Resource (disambiguation) #Service (business) #Service composition #Software Engineering (cs.SE) #Software System Performance and Reliability #Software deployment #Software engineering #Web service #World Wide Web #cs.DB #cs.SE
paper · pdf · doi:10.48550/arxiv.1805.05995
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/04/14 · openalex publication_date 2018/04/14 · arxiv updated 2018/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Data analytics on edge devices has gained rapid growth in research, industry, and different aspects of our daily life. This topic still faces many challenges such as limited computation resource on edge devices. In this paper, we further identify two main challenges: the composition and deployment of data analytics services on edge devices. We present the Zoo system to address these two challenge: on one hand, it provides simple and concise domain-specific language to enable easy and and type-safe composition of different data analytics services; on the other, it utilises multiple deployment backends, including Docker container, JavaScript, and MirageOS, to accommodate the heterogeneous edge deployment environment. We show the expressiveness of Zoo with a use case, and thoroughly compare the performance of different deployment backends in evaluation.