PLAIground: SLO-Driven Runtime Model Selection for Compound AI Systems in the Edge-Cloud-Space Continuum
arXiv:2606.14356v1 Announce Type: cross Abstract: 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 n
The proliferation of AI applications across diverse computing environments (edge, cloud, space) and the increasing demand for real-time performance and efficiency necessitate advanced solutions for managing complex AI systems.
This development addresses the critical challenge of ensuring dynamic performance and cost-effectiveness for Compound AI systems, which are becoming fundamental across various industries.
The ability to dynamically select and optimize AI models at runtime for specific Service Level Objectives (SLOs) in complex, distributed environments brings greater efficiency, reliability, and ubiquity to AI deployments.
- · Edge computing providers
- · Hyperscale cloud providers
- · AI platform developers
- · Aerospace and defense
- · Legacy fixed-model AI deployments
- · Inefficient AI infrastructure
- · High-latency systems
Improved performance and cost-efficiency of AI applications across the edge-cloud-space continuum.
Accelerated adoption of complex, multi-task AI systems in critical and real-time applications.
Enhanced operational autonomy and strategic advantage for entities capable of deploying highly adaptive and efficient AI at scale.
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Read at arXiv cs.AI