An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics

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dc.contributor.authorWang, Xin
dc.contributor.authorKhan, Azim
dc.contributor.authorWang, Jianwu
dc.contributor.authorGangopadhyay, Aryya
dc.contributor.authorBusart, Carl E.
dc.contributor.authorFreeman, Jade
dc.date.accessioned2022-06-09T19:20:50Z
dc.date.available2022-06-09T19:20:50Z
dc.date.issued2022-05-11
dc.description.abstractWith the popularity of Internet of Things (IoT), edge computing and cloud computing, more and more stream analytics applications are being developed including real-time trend prediction and object detection on top of IoT sensing data. One popular type of stream analytics is the recurrent neural network (RNN) deep learning model based time series or sequence data prediction and forecasting. Different from traditional analytics that assumes data to be processed are available ahead of time and will not change, stream analytics deals with data that are being generated continuously and data trend/distribution could change (aka concept drift), which will cause prediction/forecasting accuracy to drop over time. One other challenge is to find the best resource provisioning for stream analytics to achieve good overall latency. In this paper, we study how to best leverage edge and cloud resources to achieve better accuracy and latency for RNN-based stream analytics. We propose a novel edge-cloud integrated framework for hybrid stream analytics that support low latency inference on the edge and high capacity training on the cloud. We study the flexible deployment of our hybrid learning framework, namely edgecentric, cloud-centric and edge-cloud integrated. Further, our hybrid learning framework can dynamically combine inference results from an RNN model pre-trained based on historical data and another RNN model re-trained periodically based on the most recent data. Using real-world and simulated stream datasets, our experiments show the proposed edge-cloud deployment is the best among all three deployment types in terms of latency. For accuracy, the experiments show our dynamic learning approach performs the best among all learning approaches for all three concept drift scenarios.en_US
dc.description.sponsorshipThis work is supported by the National Science Foundation (NSF) Grant No. OAC–1942714 and U.S. Army Grant No. W911NF2120076.en_US
dc.description.urihttps://www.sciencedirect.com/science/article/pii/S0167739X22002576en_US
dc.format.extent13 pagesen_US
dc.genrejournal articlesen_US
dc.genreComputer Code
dc.identifierdoi:10.13016/m2ofv1-dihz
dc.identifier.citationWang, Xin et al. An edge–cloud integrated framework for flexible and dynamic stream analytics. Future Generation Computer Systems 137 (December 2022): 323-335. https://doi.org/10.1016/j.future.2022.07.023
dc.identifier.urihttps://doi.org/10.1016/j.future.2022.07.023
dc.identifier.urihttp://hdl.handle.net/11603/24879
dc.language.isoen_USen_US
dc.publisherElsevier
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttps://creativecommons.org/publicdomain/mark/1.0/*
dc.subjectUMBC Center for Real-time Distributed Sensing and Autonomy
dc.titleAn Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analyticsen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170en_US

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