Data analytics service composition and deployment on edge devices


Type
Conference Object
Change log
Authors
Zhao, J 
Tiplea, T 
Crowcroft, Jonathon  ORCID logo  https://orcid.org/0000-0002-7013-0121
Wang, L 
Abstract

© 2018 Copyright held by the owner/author(s). 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.

Description
Keywords
4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences
Journal Title
Big-DAMA 2018 - Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2018
Conference Name
SIGCOMM '18: ACM SIGCOMM 2018 Conference
Journal ISSN
Volume Title
Publisher
ACM
Sponsorship
EPSRC (via University of Warwick) (RESWM34910001 CONTRIVE)
EPSRC (via Queen Mary University of London (QMUL)) (ECSA1W3R)
Alan Turing Institute (unknown)