StarShip: Mitigating I/O bottlenecks in serverless computing for scientific Workflows

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. To address this challenge, we propose a novel framework, StarShip, which effectively addresses I/O bottlenecks for HPC workflows executing in serverless environments by leveraging different storage options and multi-Tier functions, co-optimizing for service time and service cost. StarShip exploits the Levenberg-Marquardt optimization method to find an effective solution in a large, complex search space. StarShip achieves significantly better performance and cost compared to competing techniques, improving service time by 45% and service cost by 37.6% on average over state-of-The-Art solutions.