@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}
}
