StarShip: Mitigating I/O Bottlenecks in Serverless Computing for Scientific Workflows

Published: 01 Jan 2024, Last Modified: 18 Jun 2024SIGMETRICS/Performance (Abstracts) 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This work highlights the significance of I/O bottlenecks that data-intensive HPC workflows face in serverless environments - an issue that has been largely overlooked by prior works. We propose StarShip, a framework that leverages different storage options and multi-tier functions to reduce I/O overhead by co-optimizing for service time and service cost. StarShip leverages the Levenberg-Marquardt optimization to find an effective solution in a large, complex search space. It outperforms existing methods with a 45% improvement in service time and a 37.6% reduction in service cost.
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