DIANA Scheduling Hierarchies for Optimizing Bulk Job Scheduling

  • Authors:

  • Ashiq Anjum;Richard McClatchey;Heinz Stockinger;Arshad Ali;Ian Willers;Michael Thomas;Muhammad Sagheer;Khawar Hasham;Omer Alvi

  • Affiliations:

  • University of the West of England, UK/ National University of Sciences and Technology, Pakistan;University of the West of England, UK;Swiss Institute of Bioinformatics, Switzerland;National University of Sciences and Technology, Pakistan;CERN, European Organization for Nuclear Research, Switzerland;California Institute of Technology, USA;National University of Sciences and Technology, Pakistan;National University of Sciences and Technology, Pakistan;National University of Sciences and Technology, Pakistan

  • Venue:
  • E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
  • Year:
  • 2006

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Abstract

The use of meta-schedulers for resource management in large-scale distributed systems often leads to a hierarchy of schedulers. In this paper, we discuss why existing meta-scheduling hierarchies are sometimes not sufficient for Grid systems due to their inability to re-organise jobs already scheduled locally. Such a job re-organisation is required to adapt to evolving loads which are common in heavily used Grid infrastructures. We propose a peer-topeer scheduling model and evaluate it using case studies and mathematical modelling. We detail the DIANA (Data Intensive and Network Aware) scheduling algorithm and its queue management system for coping with the load distribution and for supporting bulk job scheduling. We demonstrate that such a system is beneficial for dynamic, distributed and self-organizing resource management and can assist in optimizing load or job distribution in complex Grid infrastructures.