Adviser: An Intuitive Multi-Cloud Platform for Scientific and ML Workflows
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911534718910464 |
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| author | Cheng, Shihan Laurenzano, Michael A. Strauch, Brian Ellis, Timothy A. Wadhwani, Krish Hyde, David A. B. |
| author_facet | Cheng, Shihan Laurenzano, Michael A. Strauch, Brian Ellis, Timothy A. Wadhwani, Krish Hyde, David A. B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20941 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adviser: An Intuitive Multi-Cloud Platform for Scientific and ML Workflows Cheng, Shihan Laurenzano, Michael A. Strauch, Brian Ellis, Timothy A. Wadhwani, Krish Hyde, David A. B. Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science D.0; J.2 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. |
| title | Adviser: An Intuitive Multi-Cloud Platform for Scientific and ML Workflows |
| topic | Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science D.0; J.2 |
| url | https://arxiv.org/abs/2603.20941 |