We are witnessing a great awakening of AI, thanks to advances in computer hardware, breakthroughs in machine learning techniques, and the explosion of digital data. Although many enterprises are committed to using AI to deliver real business value, their endeavors are far from a guaranteed success. The adoption of AI for production use has been very slow and only a small portion of companies have revenue-generating AI systems running today. Surprisingly, difficulties in enterprise AI have little to do with core machine learning algorithms and techniques. Instead, they have a lot to do with the huge leap required from the machine learning prototypes in lab settings to large-scale production-grade AI systems. In order to adopt AI at scale and reap its full benefits, enterprises must solve problems in a wide variety of areas including infrastructure, data, insights, skills, trust, and operationalization. This talk will discuss some challenges in the areas of application infrastructure and data infrastructure for enterprise AI, and share our experiences in tackling those challenges.

Hui Lei is Vice President of AI and Data Infrastructure at Futurewei Technologies. Previously he was CTO of Watson Health Cloud at IBM, an IBM Distinguished Engineer, and an IBM Master Inventor. His research interests lie in the broad areas of data engineering and distributed systems, spanning the domains of cloud computing, mobile computing, and enterprise computing. He is a Fellow of the IEEE, a member of the IEEE Computer Society Board of Governors, Chair of the IEEE Computer Society Transactions Operations Committee, a past Editor-in-Chief of the IEEE Transactions on Cloud Computing, and an inventor of over 80 patents. He has been recognized with the IEEE Computer Society Technical Achievement Award for his pioneering work on big data engineering. He received the B.S. degree from Sun Yat-sen University, an M.S. degree from New York University, and a Ph.D. degree from Columbia University, all in Computer Science.

The increasing popularity of IoT devices allows us to communicate better, interact better, and ultimately build a new type of a scientific instrument that will allow us to explore our environment in ways that we could only dream about just a few years ago. This disruptive opportunity raises a new set of challenges: how should we manage the massive amounts of data and network traffic such instruments will eventually produce? What types of environments will be most suited to developing their full potential? What new security problems will arise? And finally: what are the best ways of leveraging intelligent edge to create new types of applications? In a research area that creates a new deployment structure, such questions are too often approached only theoretically for lack of a realistic testbed — a scientific instrument that keeps pace with the emergent requirements of science and allows researchers to deploy, measure, and analyze relevant scientific hypotheses. To help create such instruments, the NSF-funded Chameleon testbed, originally created to provide a platform for datacenter research, has now been extended to support experiments in cloud to edge. This talk will first describe Chameleon — a scientific instrument for computer science systems research, originally created to allow exploration of research topics in cloud computing such as virtualization, programmable networking, or power management - as well as its recent extension to support experimentation at the edge. It will describe the testbed capabilities required to provide a platform for the edge to cloud continuum, and give examples of edge to cloud research and education projects our users are running. Finally, it will describe tools and methodologies that Chameleon provides to improve experimental methodology and reproducibility of experiments in this environment and illustrate how a common experimentation platform can enhance sharing and scientific productivity.

Kate Keahey is one of the pioneers of infrastructure cloud computing. She created the Nimbus project, recognized as the first open source Infrastructure-as-a-Service implementation, and continues to work on research aligning cloud computing concepts with the needs of scientific datacenters and applications. To facilitate such research for the community at large, Kate leads the Chameleon project, providing a deeply reconfigurable, large-scale, and open experimental platform for Computer Science research. To foster the recognition of contributions to science made by software projects, Kate co-founded and serves as co-Editor-in-Chief of the SoftwareX journal, a new format designed to publish software contributions. Kate is a Scientist at Argonne National Laboratory and a Senior Fellow at the Computation Institute at the University of Chicago.

Fueled by computing’s omnipotence and Jevon’s paradox, the cloud’s carbon footprint continues to grow. Longtime leaders in "offset" renewable power purchases, cloud companies have announced initiatives in carbon-aware computing to move towards sustainable computing. Computing can go further, eliminating its carbon footprint and accelerating grid decarbonization. This vision is called Zero-Carbon Cloud (ZCCloud). Zero-carbon Cloud exploits a rapidly growing resource – Terawatt-hours of stranded renewable power – to eliminate computing’s carbon footprint. Stranded power arises naturally from fluctuating renewable generation and power markets. Better yet, it has zero-marginal carbon. Extensive research show that ZCCloud can support large-scale computing for scientific workloads, is cost-effective, and achieve zero carbon emissions. Several startups are actively exploring these opportunities. This talk will discuss research challenges that arise from broader application of ZCCloud ideas: How to shape cloud workloads? What new software and hardware abstractions? Datacenter designs and infrastructure? How to get and exploit dynamic carbon-emission information? How to form a partnership to decarbonize the power grid? More information is available here.

Andrew A. Chien is the William Eckhardt Professor at the University of Chicago, Director of the CERES Center for Unstoppable Computing, and a Senior Scientist at Argonne National Laboratory. Since 2017, he has served as Editor-in-Chief of the Communications of the ACM, and he currently serves on the National Science Foundation CISE Advisory Committee. Chien leads the Zero-carbon Cloud project, exploring synergies between cloud computing and the renewable power grid. Chien is a global research leader in parallel computing, computer architecture, clusters, and cloud computing, and has received numerous awards for his research. Dr. Chien served as Vice President of Research at Intel Corporation from 2005-2010, leading long-range and "disruptive technologies" research as well as worldwide academic and government partnership. From 1998-2005, he was the SAIC Chair Professor at UCSD, and prior to that a Professor at the University of Illinois. Dr. Chien is an ACM, IEEE, and AAAS Fellow, and earned his PhD, MS, and BS from the Massachusetts Institute of Technology.

This talk will discuss some insights of the new inter-disciplinary field in which we aim at engineering large-scale distributed systems composed of IoT, Edge, Fog, and Cloud (Computing Continuum). We discuss some fundamental concepts providing background for future research programs in this area. In particular, we discuss research required in Edge Intelligence.

Schahram Dustdar is Full Professor of Computer Science heading the Research Division of Distributed Systems at the TU Wien, Austria. He holds several honorary positions: University of California (USC) Los Angeles; Monash University in Melbourne, Shanghai University, Macquarie University in Sydney, University Pompeu Fabra, Barcelona, Spain. From December 2016 until January 2017 he was a Visiting Professor at the University of Sevilla, Spain and from January until June 2017 he was a Visiting Professor at UC Berkeley, USA. From 1999–2007 he worked as the co-founder and chief scientist of Caramba Labs Software AG in Vienna (acquired by Engineering NetWorld AG), a venture capital co-funded software company focused on software for collaborative processes in teams. Caramba Labs was nominated for several (international and national) awards: World Technology Award in the category of Software (2001); Top-Startup companies in Austria (CapGemini Ernst & Young) (2002); MERCUR Innovation award of the Austrian Chamber of Commerce (2002). He is co-founder of SINOAUS.net based in Nanjing, China (2020). He is founding co-Editor-in-Chief of ACM Transactions on Internet of Things (ACM TIoT) as well as Editor-in-Chief of Computing (Springer). He is an Associate Editor of IEEE Transactions on Services Computing, IEEE Transactions on Cloud Computing, ACM Computing Surveys, ACM Transactions on the Web, and ACM Transactions on Internet Technology, as well as on the editorial board of IEEE Internet Computing and IEEE Computer. Dustdar is recipient of multiple awards: IEEE TCSVC Outstanding Leadership Award (2018), IEEE TCSC Award for Excellence in Scalable Computing (2019), ACM Distinguished Scientist (2009), ACM Distinguished Speaker (2021), IBM Faculty Award (2012). He is an elected member of the Academia Europaea: The Academy of Europe, where he is chairman of the Informatics Section, as well as an IEEE Fellow (2016), an Asia-Pacific Artificial Intelligence Association (AAIA) Fellow (2021), and member of the Academy of the United Nations Sciences and Technology Organization (2021).