Speaker
Stephen Gowdy
(Fermi National Accelerator Lab. (US))
Description
The global distributed computing system (WLCG) used by the Large Hadron
Collider (LHC) is evolving. The treatment of wide-area-networking (WAN) as
a scarce resource that needs to be strictly managed is far less
necessary that originally foreseen. Static data placement and replication,
intended to limit interdependencies among computing centers, is giving way
to global data federations building on computing centers whose maturity
has increased significantly over the past decade. Different modalities for
provisioning resources, including commercial clouds, will coexist with
computing centers in our labs and universities. Compute resources may
increasingly be shared between HEP and other fields.
By necessity today's computing system is evolving in an adiabatic
fashion due to the need to support the next LHC run. In the
medium and long term, however, a number of questions arise regarding the
appropriate system architecture, for example: What ratio of storage to
compute capabilities will be needed? How should storage be deployed
geographically to maximally exploit owned, opportunistic and cloud
distributed compute capabilities? What is the right mix of placement,
WAN reads, and automated caching to optimize performance? How will the
reliability and scalability of the system be impacted by these choices?
Can different computing models or computation techniques (map reduce, etc.)
be deployed more easily with different system architectures?
In this presentation we report results from our work to simulate
future distributed computing system architectures and computing models
to answer these questions. When possible we also report our efforts
to validate this simulation using today's computing system.
Primary authors
Brian Paul Bockelman
(University of Nebraska (US))
Frank Wuerthwein
(Univ. of California San Diego (US))
Lothar A.T. Bauerdick
(Fermi National Accelerator Lab. (US))
Matevz Tadel
(Univ. of California San Diego (US))
Dr
Peter Elmer
(Princeton University (US))
Stephen Gowdy
(Fermi National Accelerator Lab. (US))
