This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co-design approaches, emphasizing usability, manageability, affordability, adaptability, efficiency, and scalability. By integrating cutting-edge technologies such as generative and agentic AI, cross-layer automation and optimization, unified control plane, and composable and adaptive system architecture, the proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness. Incorporating quantum computing as it matures will enable quantum-accelerated simulations for materials science, climate modeling, and other high-impact domains. Collaborative efforts between academia and industry are central to this vision, driving advancements in foundation models for material design and climate solutions, scalable multimodal data processing, and enhanced physics-based AI emulators for applications like weather forecasting and carbon sequestration. Research priorities include advancing AI agentic systems, LLM as an Abstraction (LLMaaA), AI model optimization and unified abstractions across heterogeneous infrastructure, end-to-end edge-cloud transformation, efficient programming model, middleware and platform, secure infrastructure, application-adaptive cloud systems, and new quantum-classical collaborative workflows. These ideas and solutions encompass both theoretical and practical research questions, requiring coordinated input and support from the research community. This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms, fostering breakthroughs in AI-driven applications and scientific discovery across academia, industry, and society.

翻译：本白皮书由IBM研究院与伊利诺伊大学厄巴纳-香槟分校研究人员在IIDAI研究所内紧密合作完成，旨在通过创新的全栈协同设计方法改造混合云系统，以应对日益复杂的AI工作负载，并着重强调可用性、可管理性、经济性、适应性、效率与可扩展性。通过整合生成式与代理式AI、跨层自动化与优化、统一控制平面以及可组合自适应系统架构等前沿技术，所提出的框架致力于解决能效、性能与成本效益方面的关键挑战。随着量子计算技术的成熟，将其纳入框架将实现材料科学、气候建模及其他高影响力领域的量子加速模拟。产学协作是这一愿景的核心，将推动材料设计与气候解决方案的基础模型、可扩展多模态数据处理以及面向天气预报与碳封存等应用的增强型物理AI模拟器的发展。研究重点包括：推进AI代理系统、作为抽象层的LLM（LLMaaA）、AI模型优化与跨异构基础设施的统一抽象、端到端边云转型、高效编程模型、中间件与平台、安全基础设施、应用自适应云系统以及新型量子-经典协同工作流。这些构想与解决方案涵盖理论与实际研究问题，需要研究界的协调投入与支持。此项联合倡议旨在将混合云打造为安全、高效、可持续的平台，推动学术界、产业界及社会各领域在AI驱动应用与科学发现方面取得突破。