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.

翻译：有效利用现代云环境的庞杂计算资源需横跨多个技术领域的专业知识：以正确参数与依赖配置科学软件、甄别数千种供应商专属实例类型与定价选项，以及管理并行或分布式执行。我们开展的一项研究表明，这些专业知识的缺失对挖掘云赋能计算科学的潜力构成持续挑战。为应对此挑战，我们提出Adviser——一个以工作流抽象为核心的多云直觉化平台。工作流是可复用的专家级构件，封装了执行科学与机器学习应用所需的环境配置、数据处理、仿真模拟、结果捕获及可视化步骤。该方案允许用户指定高层次意图，而Adviser负责资源配置、运行时部署及数据迁移。通过两个计算冰川学代码（Icepack与PISM），我们展示了如何借助Adviser获取科学洞见，并在无需云或高性能计算专业知识的情况下，快速探索成本-性能权衡及扩展行为。