Emerging data-driven scientific workflows are seeking to leverage distributed data sources to understand end-to-end phenomena, drive experimentation, and facilitate important decision-making. Despite the exponential growth of available digital data sources at the edge, and the ubiquity of non trivial computational power for processing this data, realizing such science workflows remains challenging. This paper explores a computing continuum that is everywhere and nowhere -- one spanning resources at the edges, in the core and in between, and providing abstractions that can be harnessed to support science. It also introduces recent research in programming abstractions that can express what data should be processed and when and where it should be processed, and autonomic middleware services that automate the discovery of resources and the orchestration of computations across these resources.
@article{arxiv.2406.04480,
title = {Everywhere & Nowhere: Envisioning a Computing Continuum for Science},
author = {Manish Parashar},
journal= {arXiv preprint arXiv:2406.04480},
year = {2024}
}
Comments
This paper is based on the author's IEEE Sidney Fernbach award presentation at SC23, The International Conference for High Performance Computing, Networking Storage and Analysis, Denver, CO, USA, November 2023. It has been submitted for publication to IEEE Computing in Science and Engineering