The Journal of Supercomputing

, Volume 38, Issue 3, pp 279–305

Flexible Grid service management through resource partitioning

Authors

    • Department of Information TechnologyGhent University
  • Pieter Thysebaert
    • Department of Information TechnologyGhent University
  • Marc De Leenheer
    • Department of Information TechnologyGhent University
  • Filip De Turck
    • Department of Information TechnologyGhent University
  • Bart Dhoedt
    • Department of Information TechnologyGhent University
  • Piet Demeester
    • Department of Information TechnologyGhent University
Article

DOI: 10.1007/s11227-006-8752-9

Cite this article as:
Volckaert, B., Thysebaert, P., De Leenheer, M. et al. J Supercomput (2006) 38: 279. doi:10.1007/s11227-006-8752-9

Abstract

In this paper, a distributed and scalable Grid service management architecture is presented. The proposed architecture is capable of monitoring task submission behaviour and deriving Grid service class characteristics, for use in performing automated computational, storage and network resource-to-service partitioning. This partitioning of Grid resources amongst service classes (each service class is assigned exclusive usage of a distinct subset of the available Grid resources), along with the dynamic deployment of Grid management components dedicated and tuned to the requirements of a particular service class introduces the concept of Virtual Private Grids. We present two distinct algorithmic approaches for the resource partitioning problem, the first based on Divisible Load Theory (DLT) and the second built on Genetic Algorithms (GA). The advantages and drawbacks of each approach are discussed and their performance is evaluated on a sample Grid topology using NSGrid, an ns-2 based Grid simulator. Results show that the use of this Service Management Architecture in combination with the proposed algorithms improves computational and network resource efficiency, simplifies schedule making decisions, reduces the overall complexity of managing the Grid system, and at the same time improves Grid QoS support (with regard to job response times) by automatically assigning Grid resources to the different service classes prior to scheduling.

Keywords

Grid service managementVirtual private GridService Grids

Copyright information

© Springer Science + Business Media, LLC 2006