Application of Edge-to-Cloud Methods Toward Deep Learning

被引:1
|
作者
Choudhary, Khushi [1 ]
Nersisyan, Nona [1 ]
Lin, Edward [1 ]
Chandrasekaran, Shobana [1 ]
Mayani, Rajiv [1 ]
Pottier, Loic [1 ]
Murillo, Angela P. [2 ]
Virdone, Nicole K. [1 ]
Kee, Kerk [3 ]
Deelman, Ewa [1 ]
机构
[1] Univ Southern Calif, Informat Sci Inst, Los Angeles, CA 90089 USA
[2] Indiana Univ, Sch Informat & Comp, Bloomington, IN 47405 USA
[3] Texas Tech Univ, Lubbock, TX 79409 USA
来源
2022 IEEE 18TH INTERNATIONAL CONFERENCE ON E-SCIENCE (ESCIENCE 2022) | 2022年
基金
美国国家科学基金会;
关键词
Scientific Workflows; Workflow Management Systems; Edge Computing; Pegasus; Zooplankton; Machine Learning;
D O I
10.1109/eScience55777.2022.00065
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Scientific workflows are important in modern computational science and are a convenient way to represent complex computations, which are often geographically distributed among several computers. In many scientific domains, scientists use sensors (e.g., edge devices) to gather data such as CO2 level or temperature, that are usually sent to a central processing facility (e.g., a cloud). However, these edge devices are often not powerful enough to perform basic computations or machine learning inference computations and thus applications need the power of cloud platforms to generate scientific results. This work explores the execution and deployment of a complex workflow on an edge-to-cloud architecture in a use case of the detection and classification of plankton. In the original application, images were captured by cameras attached to buoys floating in Lake Greifensee (Switzerland). We developed a workflow based on that application. The workflow aims to pre-process images locally on the edge devices (i.e., buoys) then transfer data from each edge device to a cloud platform. Here, we developed a Pegasus workflow that runs using HTCondor and leveraged the Chameleon cloud platform and its recent CHI@Edge feature to mimic such deployment and study its feasibility in terms of performance and deployment.
引用
收藏
页码:415 / 416
页数:2
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