Applications in the 3D Computing Continuum, which unifies edge, cloud, and space, require combining multiple AI tasks such as object detection, time-series analytics, and natural language processing into Compound AI systems. These systems must satisfy stringent Service Level Objectives (SLOs) on accuracy, latency, and cost. A key mechanism for maintaining SLO compliance of Compound AI systems is runtime model selection, where AI models are dynamically switched for each workflow task. However, existing distributed and compound AI frameworks do not natively support runtime model selection. We present PLAIground, a framework that enables runtime model selection for Compound AI systems. PLAIground introduces Compoundable AI Model (CAIM) abstraction, which decouples task semantics from AI model implementations via Task and Data Contracts, enabling model switching without workflow changes. Additionally, PLAIground introduces Pixie, an SLO-driven runtime model selection algorithm, which dynamically selects the most suitable model for each task during execution. Our evaluation on two realistic Compound AI workflows demonstrates that Pixie achieves up to 91.3% accuracy while maintaining SLO compliance where fixed-model strategies either violate cost and latency budgets up to 21x or miss accuracy targets by 4%.

翻译：3D计算连续体（统一了边缘、云和太空）中的应用需要将目标检测、时间序列分析和自然语言处理等多个AI任务组合成复合AI系统。这些系统必须满足对准确性、延迟和成本的严格服务等级目标（SLO）。维护复合AI系统SLO合规性的一个关键机制是运行时模型选择，即为每个工作流任务动态切换AI模型。然而，现有的分布式和复合AI框架并不原生支持运行时模型选择。我们提出PLAIground，这是一个支持复合AI系统运行时模型选择的框架。PLAIground引入了可复合AI模型（CAIM）抽象，通过任务和数据契约将任务语义与AI模型实现解耦，从而无需更改工作流即可实现模型切换。此外，PLAIground引入了Pixie，一种SLO驱动的运行时模型选择算法，可在执行过程中为每个任务动态选择最合适的模型。我们在两个真实复合AI工作流上的评估表明，Pixie在保持SLO合规性的同时实现了高达91.3%的准确性，而固定模型策略要么违反成本与延迟预算高达21倍，要么准确性目标偏差达4%。