Enabling Interoperability among Grid Meta-Schedulers

  • Authors:

  • Ivan Rodero;David Villegas;Norman Bobroff;Yanbin Liu;Liana Fong;S. Masoud Sadjadi

  • Affiliations:

  • Rutgers Discovery Informatics Institute and NSF Cloud and Autonomic Computer Center, Dept. of Electrical and Computer Engineering, Rutgers University, Piscataway, USA;School of Computing and Information Sciences, Florida International University, Miami, USA;IBM T.J. Watson Research Center, Hawthrone, USA;IBM T.J. Watson Research Center, Hawthrone, USA;IBM T.J. Watson Research Center, Hawthrone, USA;School of Computing and Information Sciences, Florida International University, Miami, USA

  • Venue:
  • Journal of Grid Computing
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

The goal of Grid computing is to integrate the usage of computer resources from cooperating partners in the form of Virtual Organizations (VO). One of its key functions is to match jobs to execution resources efficiently. For interoperability between VOs, this matching operation occurs in resource brokering middleware, commonly referred to as the meta-scheduler or meta-broker. In this paper, we present an approach to a meta-scheduler architecture, combining hierarchical and peer-to-peer models for flexibility and extensibility. Interoperability is further promoted through the introduction of a set of protocols, allowing meta-schedulers to maintain sessions and exchange job and resource state using Web Services. Our architecture also incorporates a resource model that enables an efficient resource matching across multiple Virtual Organizations, especially where the compute resources and state are dynamic. Experiments demonstrate these new functional features across three distributed organizations (BSC, FIU, and IBM), that internally use different job scheduling technologies, computing infrastructure and security mechanisms. Performance evaluations through actual system measurements and simulations provide the insights on the architecture's effectiveness and scalability.