🤖 AI Summary
This work addresses the challenge faced by scientific and machine learning researchers in efficiently leveraging multi-cloud resources for complex computations due to a lack of cloud computing expertise. To bridge this gap, we propose a workflow-centric multi-cloud platform that encapsulates low-level operations—such as environment setup, instance selection, data migration, and distributed execution—into high-level, declarative interfaces through reusable, expert-designed workflows. Users need only specify their computational intent, and the platform automatically handles cross-cloud scheduling and optimization. The system has successfully supported glaciological simulation applications such as Icepack and PISM, significantly simplifying deployment without requiring HPC or cloud specialization, while enabling efficient cost–performance trade-off analysis and scalability exploration to accelerate scientific discovery.
📝 Abstract
Effectively leveraging the vast computational resources of modern cloud environments requires expertise spanning multiple technical domains: configuring scientific software with correct parameters and dependencies, navigating thousands of provider-specific instance types and pricing options, and managing parallel or distributed execution. We conduct a study indicating that the absence of these categories of expertise poses an ongoing challenge to unlocking the potential of cloud-enabled computational science. To address this challenge, we introduce Adviser, an intuitive multi-cloud platform centered on a workflow abstraction. Workflows are reusable, expert-crafted artifacts encapsulating environment setup, data processing, simulation, result capture, and visualization steps needed to execute scientific and ML applications. This approach allows users to specify high-level intent, while Adviser handles resource provisioning, runtime configuration, and data movement. Using two computational glaciology codes, Icepack and PISM, we show how to use Adviser to gain scientific insight and perform rapid exploration of cost-performance tradeoffs and scaling behavior without specialized expertise in cloud or high-performance computing.