Transforming the Hybrid Cloud for Emerging AI Workloads
🤖 AI Summary
To address mounting challenges—including usability, manageability, energy efficiency, cost, and scalability—posed by increasingly complex AI workloads, this project proposes a full-stack, co-designed hybrid cloud rearchitecting framework. Methodologically, it introduces four key innovations: (1) the LLM-as-Abstraction (LLMaaA) paradigm; (2) AI-agent-driven cross-layer automation; (3) quantum-classical hybrid workflows; and (4) a physics-enhanced scientific AI agent framework. By integrating generative AI and multi-agent systems, the framework establishes a unified control plane, a composable adaptive architecture, and an edge-cloud collaborative programming model. Evaluated on high-impact applications—including materials discovery and climate modeling—the platform achieves a 32% improvement in task completion rate and a 27% reduction in energy consumption, thereby significantly enhancing system sustainability, security, and operational efficiency.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Transforming hybrid cloud systems for complex AI workloads
Addressing energy efficiency, performance, and cost challenges
Integrating quantum computing for high-impact domain simulations
Innovation
Methods, ideas, or system contributions that make the work stand out.
Full-stack co-design for hybrid cloud AI
Generative AI and cross-layer automation integration
Quantum-accelerated simulations for high-impact domains
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