@article {RN208,
	title = {Leveraging user access patterns and advanced cyberinfrastructure to accelerate data delivery from shared-use scientific observatories},
	journal = {Future Generation Computer Systems},
	year = {2021},
	type = {Journal Article},
	abstract = {With the growing number and increasing availability of shared-use instruments and observatories, observational data is becoming an essential part of application workflows and contributor to scientific discoveries in a range of disciplines. However, the corresponding growth in the number of users accessing these facilities coupled with the expansion in the scale and variety of the data, is making it challenging for these facilities to ensure their data can be accessed, integrated, and analyzed in a timely manner, and is resulting significant demands on their cyberinfrastructure (CI).

In this paper, we present the design of a push-based data delivery framework that leverages emerging in-network capabilities, along with data pre-fetching techniques based on a hybrid data management model. Specifically, we analyze data access traces for two large-scale observatories, Ocean Observatories Initiative (OOI) and Geodetic Facility for the Advancement of Geoscience (GAGE), to identify typical user access patterns and to develop a model that can be used for data pre-fetching. Furthermore, we evaluate our data pre-fetching model and the proposed framework using a simulation of the Virtual Data Collaboratory (VDC) platform that provides in-network data staging and processing capabilities. The results demonstrate that the ability of the framework to significantly improve data delivery performance and reduce network traffic at the observatories{\textquoteright} facilities.},
	keywords = {Cyberinfrastructure, Data pre-fetching, Distributed data sharing, Distributed facilities, Observatories, Virtual Data Collaboratory},
	doi = {10.1016/j.future.2021.03.004},
	url = {https://app.dimensions.ai/details/publication/pub.1136656679},
	author = {Qin, Yubo and Rodero, Ivan and Simonet, Anthony and Meertens, Charles and Reiner, Daniel and Riley, James and Parashar, Manish}
}
@article {RN166,
	title = {The Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data-Driven Research},
	journal = {Computing},
	volume = {22},
	number = {3},
	year = {2020},
	pages = {79-92},
	type = {Journal Article},
	abstract = {The Virtual Data Collaboratory is a federated data cyberinfrastructure designed to drive data-intensive, interdisciplinary, and collaborative research that will impact researchers, educators, and entrepreneurs across a broad range of disciplines and domains as well as institutional and geographic boundaries.},
	keywords = {big data, Cloud computing, Collaboration, Computer architecture, Education, Software engineering, Virtual reality},
	doi = {10.1109/mcse.2019.2908850},
	url = {https://app.dimensions.ai/details/publication/pub.1113521625},
	author = {Parashar, Manish and Simonet, Anthony and Rodero, Ivan and Ghahramani, Forough and Agnew, Grace and Jantz, Ron and Honavar, Vasant}
}
@article {RN144,
	title = {Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {33},
	number = {6},
	year = {2019},
	pages = {1159-1174},
	type = {Journal Article},
	abstract = {Dramatic changes in the technology landscape marked by increasing scales and pervasiveness of compute and data have resulted in the proliferation of edge applications aimed at effectively processing data in a timely manner. As the levels and fidelity of instrumentation increases and the types and volumes of available data grow, new classes of applications are being explored that seamlessly combine real-time data with complex models and data analytics to monitor and manage systems of interest. However, these applications require a fluid integration of resources at the edge, the core, and along the data path to support dynamic and data-driven application workflows, that is, they need to leverage a computing continuum. In this article, we present our vision for enabling such a computing continuum and specifically focus on enabling edge-to-cloud integration to support data-driven workflows. The research is driven by an online data-driven tsunami warning use case that is supported by the deployment of large-scale national environment observation systems. This article presents our overall approach as well as current status and next steps.},
	keywords = {Continuum computing, data-driven workflows, edge-to-cloud integration, programming systems, streaming data-analytics},
	doi = {10.1177/1094342019877383},
	url = {https://app.dimensions.ai/details/publication/pub.1121117342},
	author = {Balouek-Thomert, Daniel and Renart, Eduard Gibert and Zamani, Ali Reza and Simonet, Anthony and Parashar, Manish}
}
