Speaker
R. Cavanaugh
(UNIVERSITY OF FLORIDA)
Description
A grid consists of high-end computational, storage, and network resources that,
while known a priori, are dynamic with respect to activity and availability.
Efficient co-scheduling of requests to use grid resources must adapt to this
dynamic environment while meeting administrative policies. We discusses
the necessary requirements of such a scheduler and introduce a distributed
framework called SPHINX that schedules complex, data intensive High Energy
Physics and Data Mining applications in a grid environment, respecting local and
global policies along with a specified level of quality of service. The SPHINX
design allows for a number of functional modules and/or distributed services to
flexibly schedule workflows representing multiple applications on grids. We
present experimental results for SPHINX that effectively utilize existing grid
middleware such as monitoring and workflow management/execution systems. These
results demonstrate that SPHINX can successfully schedule work across a large
number of grid sites that are owned by multiple units in a virtual organization.
Primary authors
J.U. In
(UNIVERSITY OF FLORDIA)
L. Chitnis
(University of Florida)
M. Kulkarni
(UNIVERSITY OF FLORIDA)
P. Avery
(UNIVERSITY OF FLORIDA)
R. Cavanaugh
(UNIVERSITY OF FLORIDA)
S. Ranka
(UNIVERSITY OF FLORIDA)
