Intelligent Optimization Strategies for Hierarchical Cloud-Edge Computing in Heterogeneous Hardware Ecosystems

With the rapid development of artificial intelligence, big data analytics, and high-performance computing, traditional homogeneous architectures are increasingly unable to meet the growing computational demands, particularly in parallel computing and immediate data processing scenarios. The application of heterogeneous hardware technology has overcome the bottlenecks in computational power and energy efficiency, and has been widely adopted in cloud and edge computing domains. However, challenges such as the complexity of heterogeneous hardware co-design, fragmentation of software ecosystems, and resource allocation optimization remain significant. This paper provides an in-depth analysis of the divergence trends of cloud and edge computing within the heterogeneous hardware ecosystem. Additionally, it explores the root causes and impacts of the complexity of heterogeneous hardware co-design, the fragmentation of the software ecosystem, and highlights the fundamental challenges and latest progress in resource allocation optimization. The paper further proposes a hierarchical architecture and practical potential for intelligent methods in heterogeneous hardware co-design: local optimization based on mathematical modeling, global optimization driven by biomimetics and deep learning, and domain-specific optimization supported by large language models. Together, these form a three-tiered collaborative optimization system. This “AI deploying AI” technological paradigm, through dynamic perception-decision feedback loops, provides a systematic breakthrough path for addressing the fragmentation of the hardware ecosystem and enhancing cross-hardware resource utilization.