Extreme Scale Data Management for In-Situ Scientific
Workflows
Author/Presenter
Event Type
Workshop
TimeMonday, November 13th9:10am -
10am
Location501
DescriptionData staging and in-situ/in-transit data processing are
emerging as attractive approaches for supporting extreme
scale scientific workflows. These approaches can improve
end-to-end performance by enabling efficient data
sharing between coupled simulations and data analytics
components of an in-situ workflow. However, complex and
dynamic data access/exchange patterns coupled with
architectural trends toward smaller memory per core and
deeper memory hierarchies threaten to impact the
effectiveness of this approach. In this talk, I will
explore a policy-based autonomic data management
approach that can adaptively respond at runtime to
dynamic data management requirements. Specifically, I
will formulate the autonomic data management approach
and present the design and implementation of autonomic
policies as well as cross layer mechanisms, and will
experimentally demonstrate how these autonomic
adaptations can tune the application behaviors and
resource allocations at runtime while meeting the data
management requirements and constraints. This research
is part of the DataSpaces project at the Rutgers
Discovery Informatics Institute.
Author/Presenter
