Data-Parallel and Heterogeneity-Aware Scheduling for Geo-Distributed Serverless Scientific Workflows
Serverless computing has emerged as a promising paradigm for executing scientific workflows characterized by complex task dependencies, data-intensive operations, and high computational demands. However, most existing scheduling approaches assume homogeneous, single-region environments and primarily focus on isolated f...