Performance prediction based resource selection in grid environments

Authors:
Peggy Lindner;Edgar Gabriel;Michael M. Resch
Affiliations:
High Performance Computing Center Stuttgart, University of Stuttgart, Stuttgart, Germany;Parallel Software Technologies Laboratory, Department of Computer Science, University of Houston, Houston, TX;High Performance Computing Center Stuttgart, University of Stuttgart, Stuttgart, Germany
Venue:
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
Year:
2007

Citing 6
Cited 2

A Parallel Version of the Quasi-Minimal Residual Method, Based on Coupled Two-Term Recurrences

PARA '96 Proceedings of the Third International Workshop on Applied Parallel Computing, Industrial Computation and Optimization
The Globus Project: A Status Report

HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Grid resource management in legion

Grid resource management
A Grid service broker for scheduling e-Science applications on global data Grids: Research Articles

Concurrency and Computation: Practice & Experience - Middleware for Grid Computing
GCM: a grid configuration manager for heterogeneous grid environments

International Journal of Grid and Utility Computing
Software development in the grid: the DAMIEN tool-set

ICCS'03 Proceedings of the 1st international conference on Computational science: PartI

On-line feedback-based automatic resource configuration for distributed applications

Cluster Computing
Estimation of MPI application performance on volunteer environments

Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Deploying Grid technologies by distributing an application over several machines has been widely used for scientific simulations, which have large requirements for computational resources. The Grid Configuration Manager (GCM) is a tool developed to ease the management of scientific applications in distributed environments and to hide some of the complexities of Grids from the end-user. In this paper we present an extension to the Grid Configuration Manager in order to incorporate a performance based resource brokering mechanism. Given a pool of machines and a trace file containing information about the runtime characteristics of the according application, GCM is able to select the combination of machines leading to the lowest execution time of the application, taking machine parameters as well as the network interconnect between the machines into account. The estimate of the execution time is based on the performance prediction tool Dimemas. The correctness of the decisions taken by GCM is evaluated in different scenarios.