Browsing by Author "Deelman, Ewa"

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    Application of Edge-to-Cloud Methods Toward Deep Learning
    (IEEE, 2022-10) Choudhary, Khushi; Nersisyan, Nona; Lin, Edward; Chandrasekaran, Shobana; Mayani, Rajiv; Pottier, Loic; Murillo, Angela P.; Virdone, Nicole K.; Kee, Kerk; Deelman, Ewa; Library and Information Science, School of Computing and Informatics
    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.
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    Blueprint: Cyberinfrastructure Center of Excellence
    (Zenodo, 2021-03) Deelman, Ewa; Mandal, Anirban; Murillo, Angela P.; Nabrzyski, Jarek; Pascucci, Valerio; Ricci, Robert; Baldin, Ilya; Sons, Susan; Christopherson, Laura; Vardeman, Charles; Ferreira da Silva, Rafael; Wyngaard, Jane; Petruzza, Steve; Rynge, Mats; Vahi, Karan; Whitcup, Wendy R.; Drake, Josh; Scott, Erik; Library and Information Science, School of Informatics and Computing
    In 2018, NSF funded an effort to pilot a Cyberinfrastructure Center of Excellence (CI CoE or Center) that would serve the cyberinfrastructure (CI) needs of the NSF Major Facilities (MFs) and large projects with advanced CI architectures. The goal of the CI CoE Pilot project (Pilot) effort was to develop a model and a blueprint for such a CoE by engaging with the MFs, understanding their CI needs, understanding the contributions the MFs are making to the CI community, and exploring opportunities for building a broader CI community. This document summarizes the results of community engagements conducted during the first two years of the project and describes the identified CI needs of the MFs. To better understand MFs' CI, the Pilot has developed and validated a model of the MF data lifecycle that follows the data generation and management within a facility and gained an understanding of how this model captures the fundamental stages that the facilities' data passes through from the scientific instruments to the principal investigators and their teams, to the broader collaborations and the public. The Pilot also aimed to understand what CI workforce development challenges the MFs face while designing, constructing, and operating their CI and what solutions they are exploring and adopting within their projects. Based on the needs of the MFs in the data lifecycle and workforce development areas, this document outlines a blueprint for a CI CoE that will learn about and share the CI solutions designed, developed, and/or adopted by the MFs, provide expertise to the largest NSF projects with advanced and complex CI architectures, and foster a community of CI practitioners and researchers.