Resource Matching in Non-dedicated Multicluster Environments

  • Authors:

  • Josep Lluis Lérida;Francesc Solsona;Francesc Giné;Jose Ramon García;Porfidio Hernández

  • Affiliations:

  • Departamento de Informática e Ingeniería Industrial, Universitat de Lleida, Spain;Departamento de Informática e Ingeniería Industrial, Universitat de Lleida, Spain;Departamento de Informática e Ingeniería Industrial, Universitat de Lleida, Spain;Departamento de Arquitectura y Sistemas Operativos, Universitat Autònoma de Barcelona, Spain;Departamento de Arquitectura y Sistemas Operativos, Universitat Autònoma de Barcelona, Spain

  • Venue:
  • High Performance Computing for Computational Science - VECPAR 2008
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

We are interested in making use of Multiclusters to execute parallel applications. The present work is developed within the M-CISNE project. M-CISNE is a non-dedicated and heterogeneous Multicluster environment which includes MetaLoRaS, a two-level MetaScheduler that manages the appropriate job allocation to available resources. In this paper, we present a new resource-matching model for MetaLoRaS, which is aimed at mitigating the degraded turnaround time of co-allocated jobs, caused by the contention on shared inter-cluster links. The model is linear programming based and considers the availability of computational resources and the contention of shared inter and intra-cluster links. Its goal is to minimize the average turnaround time of the parallel applications without disturbing the local applications excessively and maximize the prediction accuracy. We also present a parallel job model that takes both computation and communication characterizations into account. By doing this, greater accuracy is obtained than in other models only focused on one of these characteristics. Our preliminary performance results indicate that the linear programming model for on-line resource matching is efficient in speed and accuracy and can be successfully applied to co-allocate jobs across different clusters.