Adviser: An Intuitive Multi-Cloud Platform for Scientific and ML Workflows
This addresses the problem of accessing cloud computational resources for scientists and ML practitioners, but it is incremental as it builds on existing workflow and cloud management concepts.
The paper tackles the challenge of leveraging cloud resources for scientific and ML workflows by introducing Adviser, a multi-cloud platform that uses workflow abstractions to simplify resource provisioning and configuration, enabling users to perform tasks like cost-performance tradeoffs and scaling analysis without specialized expertise.
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.