Efficient Resource Matching in Heterogeneous Grid Using Resource Vector

In this paper, a method for efficient scheduling to obtain optimum job throughput in a distributed campus grid environment is presented; Traditional job schedulers determine job scheduling using user and job resource attributes. User attributes are related to current usage, historical usage, user priority and project access. Job resource attributes mainly comprise of soft requirements (compilers, libraries) and hard requirements like memory, storage and interconnect. A job scheduler dispatches jobs to a resource if a job's hard and soft requirements are met by a resource. In current scenario during execution of a job, if a resource becomes unavailable, schedulers are presented with limited options, namely re-queuing job or migrating job to a different resource. Both options are expensive in terms of data and compute time. These situations can be avoided, if the often ignored factor, availability time of a resource in a grid environment is considered. We propose resource rank approach, in which jobs are dispatched to a resource which has the highest rank among all resources that match the job's requirement. The results show that our approach can increase throughput of many serial / monolithic jobs.Comment: 10 page

Replay of digitally-recorded holograms using a computational grid

Since the calculations are independent, each plane within an in-line digital hologram of a particle field can be reconstructed by a separate computer. We investigate strategies to reproduce a complete sample volume as quickly and efficiently as possible using Grid computing. We used part of the EGEE Grid to reconstruct multiple sets of planes in parallel across a wide-area network, and collated the replayed images on a single Storage Element such that a subsequent particle tracking and analysis code might then be run. Although most of the sample volume is generated up to 20 times faster on a Grid, there are some stragglers which cause the reconstruction rate to slow, and a significant proportion of jobs get lost completely, leaving blocks missing from the sample volume. In the light of these experimental findings we propose some strategies for making Grid computing useful in the field of digital hologram reconstruction and analysis

Grid computing for the numerical reconstruction of digital holograms

Digital holography has the potential to greatly extend holography's applications and move it from the lab into the field: a single CCD or other solid-state sensor can capture any number of holograms while numerical reconstruction within a computer eliminates the need for chemical processing and readily allows further processing and visualisation of the holographic image. The steady increase in sensor pixel count and resolution leads to the possibilities of larger sample volumes and of higher spatial resolution sampling, enabling the practical use of digital off-axis holography. However this increase in pixel count also drives a corresponding expansion of the computational effort needed to numerically reconstruct such holograms to an extent where the reconstruction process for a single depth slice takes significantly longer than the capture process for each single hologram. Grid computing - a recent innovation in largescale distributed processing -provides a convenient means of harnessing significant computing resources in an ad-hoc fashion that might match the field deployment of a holographic instrument. In this paper we consider the computational needs of digital holography and discuss the deployment of numericals reconstruction software over an existing Grid testbed. The analysis of marine organisms is used as an exemplar for work flow and job execution of in-line digital holography