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arXiv · 2411.13239

Transforming the Hybrid Cloud for Emerging AI Workloads

Deming Chen·Alaa Youssef·Ruchi Pendse·André Schleife·Bryan K. Clark·Hendrik Hamann·Jingrui He·Teodoro Laino·Lav Varshney·Yuxiong Wang·Avirup Sil·Reyhaneh Jabbarvand·Tianyin Xu·Volodymyr Kindratenko·Carlos Costa·Sarita Adve·Charith Mendis·Minjia Zhang·Santiago Núñez-Corrales·Raghu Ganti·Mudhakar Srivatsa·Nam Sung Kim·Josep Torrellas·Jian Huang·Seetharami Seelam·Klara Nahrstedt·Tarek Abdelzaher·Tamar Eilam·Huimin Zhao·Matteo Manica·Ravishankar Iyer·Martin Hirzel·Vikram Adve·Darko Marinov·Hubertus Franke·Hanghang Tong·Elizabeth Ainsworth·Han Zhao·Deepak Vasisht·Minh Do·Sahil Suneja·Fabio Oliveira·Giovanni Pacifici·Ruchir Puri·Priya Nagpurkar
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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.

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Deming Chen, Alaa Youssef, Ruchi Pendse, André Schleife, Bryan K. Clark, Hendrik Hamann, Jingrui He, Teodoro Laino, Lav Varshney, Yuxiong Wang, Avirup Sil, Reyhaneh Jabbarvand, Tianyin Xu, Volodymyr Kindratenko, Carlos Costa, Sarita Adve, Charith Mendis, Minjia Zhang, Santiago Núñez-Corrales, Raghu Ganti, Mudhakar Srivatsa, Nam Sung Kim, Josep Torrellas, Jian Huang, Seetharami Seelam, Klara Nahrstedt, Tarek Abdelzaher, Tamar Eilam, Huimin Zhao, Matteo Manica, Ravishankar Iyer, Martin Hirzel, Vikram Adve, Darko Marinov, Hubertus Franke, Hanghang Tong, Elizabeth Ainsworth, Han Zhao, Deepak Vasisht, Minh Do, Sahil Suneja, Fabio Oliveira, Giovanni Pacifici, Ruchir Puri, Priya Nagpurkar. 2024-11-20. Transforming the Hybrid Cloud for Emerging AI Workloads. https://arxiv.org/abs/2411.13239

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