SCOPE
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Large-scale scientific computing systems have become so complex, autonomic management is required to deploy, operate, and tune. Yet the current state-of-the-art for managing high performance and distributed computing infrastructures does not leverage the recent advances in machine learning to more accurately predict, diagnose, and improve systems in response to user computation and system health. This workshop is focused on the emerging science of autonomic management and optimization of large, distributed scientific computing systems.
Complex scientific workflows consist of thousands of interconnected systems that are geographically distributed. With resources from telescopes and light sources to fast networks and smart IoT sensor systems, it is clear that a single, centralized, operational team and software stack cannot coordinate and manage all of the resources. Instead, resources must begin to respond autonomically, managing and tuning their behavior in response to scientific workflows. The objective of this workshop is to discuss new approaches and methods to make the science ecosystem smart by incorporating the functions of sensing, intelligence, and control. We intend to bring together researchers working on smart and autonomic computing and communication systems, researchers working on middleware and tools to enable distributed science, developers that build distributed science workflows and science users. By bringing together these stakeholders, we hope to foster active discussions, understand the gaps between existing components in distributed science ecosystems and make progress towards bridging the gaps to realize a smart cyberinfrastructure for science.
Important Dates

SUBMISSIONS
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All papers must be original and not simultaneously submitted to another journal or conference. Authors are invited to submit either a full (8-page) paper or a short/work-in-progress (4-page) paper. Submission link: https://easychair.org/conferences/?conf=aiscience19
Formatting
Authors are invited to submit papers with unpublished, original work of not more than 8 pages of double column text using single spaced 10 point size on 8.5 x 11 inch pages (including all text, figures, and references), as per ACM 8.5 x 11 manuscript guidelines (document templates can be found at http://www.acm.org/sigs/publications/proceedings-templates). Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM digital library.

TOPICS
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* autonomic cyberinfrastructure
* Smart HPC, storage and networks
* Intelligent distributed workflow applications
* Real-time, streaming-based data processing and analysis
* Light-weight machine learning
* Networked autonomic systems and consensus
* Smart instruments, edge-systems, and IoTs
* Data monitoring, instrumentation and management for smart systems
* Peer-to-peer communication for networked autonomic systems
* Data analytics for edge systems and IoTs

ORGANIZERS
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Program Committee
* Pete Beckman, Argonne National Laboratory, USA
* Alok Choudhary, Northwestern University, USA
* Dipak Ghosal, University of California, Davis, USA
* Hai Jin, Huazhong University of Science and Technology, China
* Eun-Sung Jung, Hongik University, South Korea
* Raj Kettimuthu, Argonne National Laboratory, USA
* Jinoh Kim, Texas A&M University, USA
* Mariam Kiran, Energy Sciences Network, USA
* Kerstin Kleese van Dam, Brookhaven National Laboratory, USA
* Wei-keng Liao, Northwestern University, USA
* Nagi Rao, Oak Ridge National Laboratory, USA
* Eric Rutten, INRIA, France
* Alex Sim, Lawrence Berkeley National Laboratory, USA
* John Wu, Lawrence Berkeley National Laboratory, USA
* Ramin Yahyapour, University of Göttingen, Germany
* Alessandro Vittorio Papadopoulos, Malardalen University, Sweden
* Swann Perarnau, Argonne National Laboratory, USA
* Ming Zhao, Arizona State University, USA
* Erik Elmroth, Umeå University, Sweden

Organizing committee
* Pete Beckman, Argonne National Laboratory, USA
* Raj Kettimuthu, Argonne National Laboratory, USA
* Alex Sim, Lawrence Berkeley National Laboratory, USA
* Eric Rutten, INRIA, France

Publicity Chair
* Zhengchun, University of Chicago and Argonne National Laboratory, USA

CONTACT
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All questions about submissions should be emailed to ai-science@mcs.anl.gov. More information is available at https://ai-science.github.io/.