Toward System-of-Systems Integration for Composable Cloud-HPC-Edge AI Platforms

The paper arXiv:2609.33427v1, titled "Toward System-of-Systems Integration for Composable Cloud-HPC-Edge AI Platforms," was submitted on September 27, 2026, by authors Sumit Rakesh and Rajkumar Saini. It proposes viewing modern AI infrastructure as a System-of-Systems (SoS) where cloud, HPC, and edge components retain federated control while being converged in use.

The authors introduce composable integration as a method to coordinate these independently controlled systems through explicit interfaces, contracts, and mappings rather than a single orchestration stack. Key contributions include a peer constituent-system model, a boundary test to distinguish systems from components, and seven integration surfaces that preserve native authority while enabling cross-system workflows.

This material addresses the growing mismatch between AI platform needs and the heterogeneous infrastructure stacks on which modern AI systems are built. It argues that contemporary AI workloads are no longer served well by a single, self-contained platform model: training, inference, data preparation, fine-tuning, and edge deployment may simultaneously involve cloud service platforms, HPC workload-management systems, cloud-native orchestration, data and artifact management, managed connectivity, observability, and cyber-physical or edge environments. Each of these subsystems is designed around different assumptions about scheduling, tenancy, resource semantics, failure behavior, data locality, lifecycle management, and operational control. The paper’s central premise is that integrating these domains requires a system-of-systems perspective rather than treating one stack as the primary platform and the others as peripheral components.

Its key contribution is a conceptual and architectural framing for composable cloud-HPC-edge AI platforms, in which heterogeneous subsystems are integrated through well-defined interfaces, shared operational semantics, and cross-cutting governance mechanisms. Rather than focusing only on API compatibility, the work emphasizes deeper interoperability: how workload intent is expressed, how resources are provisioned and scheduled across different execution domains, how data and model artifacts move with provenance and policy constraints, and how observability, security, and lifecycle management are coordinated across boundaries. This framing positions composition as a first-class design requirement, enabling AI platforms to mix cloud elasticity, HPC throughput, edge proximity, and managed services without collapsing into brittle, monolithic architectures.

The material matters because it speaks directly to a practical and increasingly urgent problem in AI infrastructure design. As AI systems expand beyond centralized training into distributed inference, on-prem HPC bursts, edge adaptation, and cyber-physical deployment, organizations must reuse existing stacks while maintaining coherent operational control. A system-of-systems approach offers a more realistic path toward portability, resilience, cost efficiency, and governance than attempts to force all workloads into one dominant model. For platform architects, HPC operators, cloud-native engineers, and AI systems researchers, the paper provides a useful lens for designing AI infrastructure that is composable by design rather than retrofitted after the fact.