Speaker
A. Anjum
(NIIT)
Description
Grid computing provides key infrastructure for distributed problem solving in
dynamic virtual organizations. However, Grids are still the domain of a few highly
trained programmers with expertise in networking, high-performance computing, and
operating systems.
One of the big issues in the full-scale usage of a grid is the matching of the
resource requirements of a job submission to available resources. In order for
resource brokers/job schedulers to ensure efficient use of grid resources, an
initial estimate of the likely resource usage of a submission must be made. In the
context of the Grid Enabled Analysis Environment (GAE), physicists want the ability
to discover, acquire, and reliably manage computational resources dynamically, in
the course of their everyday activities. They do not want to be bothered with the
location of these resources, the mechanisms that are required to use them, keeping
track of the status of computational tasks operating on these resources, or with
reacting to failure. They do care about how long their tasks are likely to run and
how much these tasks will cost.
So the grid scheduler must have the capability to estimate before job submission,
how much time and resources the job will consume on execution site. Our proposed
module, Prediction engine will be part of scheduler and it will provide estimates of
resource use along with the duration of use. This will enable scheduler to choose
the optimum site for job execution.
This paper presents the survey of existing grid schedulers and then based on this
survey states the need for resource usage estimation. Also the architecture and
design of “grid prediction engine” that predicts the resource requirements of a job
submission is discussed.
Primary authors
A. Ali
(NIIT)
A. Anjum
(NIIT)
A. Mehmood
(NIIT)
H. Newman
(Caltech)
I. Willers
(Caltech)
J. Bunn
(Caltech)
R. Meclatchey
(UWE)
