Adviser: An Intuitive Multi-Cloud Platform for Scientific and ML Workflows

TL;DR

Adviser is a user-friendly multi-cloud platform that simplifies scientific and machine learning workflows by automating resource management and execution, enabling researchers to focus on scientific insights.

Contribution

The paper introduces Adviser, a novel workflow abstraction platform that automates cloud resource provisioning and execution for scientific and ML applications, reducing the need for cloud expertise.

Findings

Enables rapid exploration of cost-performance tradeoffs.

Supports scientific insight generation with minimal cloud expertise.

Demonstrated effectiveness with glaciology codes Icepack and PISM.

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.