Architecting Intelligent Computing Ecosystems: A Comprehensive Study on the Convergence of Edge, Cloud, and Cognitive Infrastructures for Next-Generation Digital Systems

Abstract

This study proposes a unifying architecture for intelligent computing ecosystems that converge edge, cloud, and cognitive infrastructures to power next-generation digital systems. We synthesize design principles spanning heterogeneous accelerators at the edge, elastic multi-cloud backplanes, and AI/ML services (training, inference, and knowledge orchestration) to deliver low-latency, trustworthy, and sustainable intelligence at scale. The paper contributes: (i) a layered reference architecture device/edge, regional micro-cloud, core cloud, and cognition plane integrated via a zero-trust service mesh and data-product interfaces; (ii) an operations blueprint combining event-driven pipelines, MLOps, and federated learning with privacy-preserving analytics; and (iii) a governance model aligning data lineage, policy-as-code, and cost/energy telemetry with SLOs. Using representative workloads (industrial vision, real-time personalization, digital twins, and RAG-based assistants), we detail workload placement strategies, accelerator scheduling, and serverless patterns for bursty demand. Prototype deployments demonstrate how intent-based orchestration, observability, and feedback loops (A/B, shadow, and canary) improve end-to-end responsiveness, resilience, and resource efficiency while maintaining compliance. We discuss portability across 5G/6G networks, blue/green upgrades for AI models, and failure domains spanning device to cloud. Finally, we outline a research agenda on cross-layer optimization, energy-aware scheduling, foundation-model safety at the edge, and standardized interfaces for interoperable cognition services. The result is a practical roadmap for architects to build secure, adaptive, and cost-effective intelligent systems that learn continuously and act in real time