First International Workshop on Workflow Science (WoWS 2017)

In conjunction with the Thirteenth IEEE eScience Conference in Auckland, New Zealand from 24 – 27 October 2017.

 

Many science communities depend on large, collocated or distributed computing infrastructures for their scientific discoveries rather than on small, single-user machines or clusters. As data analysis tasks and data volumes grow larger and more complex, the software infrastructure required to manage the data, executables, and resources are likewise growing in sophistication and capability. Scientific workflows simplify a series of complex tasks and manage a collection of infrastructure components to accomplish a scientific task. Predicting and/or explaining the performance of scientific workflows is an outstanding problem for scientists, tool developers and resource administrators. Although it is much more art than science at the moment, there are research efforts to scientifically analyze workflow behaviour and performance, that we call “workflow science” or “the science of scientific workflows”. Understanding the behavior of individual tasks and the interaction among the tasks are some of the challenges this new research area focuses on, with an ultimate goal to improve the performance of workflows and utilization of the systems surrounding these workflows. This workshop will provide a forum to advance research that will significantly enhance our ability to predict how scientific workflows perform and to explain workflow behavior on distributed infrastructure comprising of computers, storage, instruments and multi-gigabit networks. The workshop envisions the study of workflows as a science of its own with theory, experimentation and applications, and accepts papers in all these three areas.

Topics of interest include, but is not limited to:

  • Analytical and data-driven models of workflow components
  • Integrated end-to-end workflow performance models including tools, data and cyberinfrastructure
  • Workflow performance monitoring, prediction and optimization tools
  • Dynamic data-driven workflow schedulers 
  • Interaction of systems and workflows
  • Analytical models for workflows on experimental, observational and simulation facilities
  • Advanced workflow applications that span data, compute and network systems and tools (pros/cons/lessons learned)
  • Vision papers on types of science that benefit from workflow approach
  • Future of workflows, research directions and vision and advanced technologies
  • Instrumentation of workflows and infrastructure components for data collection, correlation, and aggregation

Important Dates:

Submissions Due: Friday 28 July 2017
Notification of Acceptance: Wednesday 9 August 2017

Submissions:

Authors are invited to submit unpublished, original work, using the IEEE 8.5 × 11 manuscript guidelines: double-column text using single-spaced 10 point font on 8.5 × 11 inch pages. Templates are available from hereThe conference proceedings will be made available online through the IEEE Digital Library. Authors should submit papers hereSubmissions will be fully peer-reviewed. It is a requirement that at least one author of each accepted paper attend the conference.

Organizers:

Ilkay Altintas, San Diego Supercomputer Center, UC San Diego, USA
Raj Kettimuthu, Argonne National Laboratory and The University of Chicago, USA
Craig E. Tull, Lawrence Berkeley National Laboratory, USA

Program Committee:

Moustafa AbdelBaky, Rutgers University
Shantenu Jha, Rutgers University
Daniel Katz, University of Illinois at Urbana-Champaign
Scott Klasky, Oak Ridge National Laboratory
Kerstin Kleese van Dam, Brookhaven National Laboratory
Manish Parashar, Rutgers University
Douglas Thain, University of Notre Dame
Jianwu Wang, University of Maryland, Baltimore County

Steering Committee:

Rich Carlson, DOE Office of Science, Advanced Scientific Computing Research, USA
Ewa Deelman, University of Southern California, USA
Ian Foster, University of Chicago
Darren Kerbyson, Pacific Northwest National National Laboratory, USA
Erich Strohmaier, Lawrence Berkeley National Laboratory, USA