Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case
arXiv:2603.12001v2 Announce Type: replace-cross Abstract: Distributed AI and IoT applications increasingly execute across heterogeneous resources spanning end devices, edge/fog infrastructure, and cloud platforms, often under different administrative domains. Fluid Computing has emerged as a promising paradigm for enhancing massive resource management across the computing continuum by treating such resources as a unified fabric, enabling optimal service-agnostic deployments driven by application requirements. However, existing solutions remain largely centralized and often do not explicitly ad
The proliferation of distributed AI and IoT applications across diverse computational environments necessitates more robust and secure orchestration methods.
This development addresses critical challenges in managing heterogeneous computing resources, which is essential for scalable and secure AI deployments.
Current centralized orchestration models are becoming less viable as AI and IoT systems expand across edge, fog, and cloud infrastructures under varied administrative controls.
- · Distributed AI developers
- · IoT device manufacturers
- · Edge computing providers
- · Companies operating across multiple cloud environments
- · Providers of purely centralized AI orchestration platforms
- · Legacy IT infrastructure without distributed capabilities
Improved efficiency and security for AI applications deployed across complex, multi-domain computing environments.
Accelerated development of resilient and adaptable AI systems, reducing dependency on single points of failure.
Potential for new business models around distributed AI services and highly fluid compute resource markets.
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Read at arXiv cs.LG