A case for cooperative and incentive-based federation of distributed clusters

  • Authors:

  • Rajiv Ranjan;Aaron Harwood;Rajkumar Buyya

  • Affiliations:

  • GRIDS Lab and P2P Group, Department of Computer Science and Software Engineering, University of Melbourne, Victoria, Australia;GRIDS Lab and P2P Group, Department of Computer Science and Software Engineering, University of Melbourne, Victoria, Australia;GRIDS Lab and P2P Group, Department of Computer Science and Software Engineering, University of Melbourne, Victoria, Australia

  • Venue:
  • Future Generation Computer Systems
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

Research interest in Grid computing has grown significantly over the past five years. Management of distributed resources is one of the key issues in Grid computing. Central to management of resources is the effectiveness of resource allocation as it determines the overall utility of the system. The current approaches to brokering in a Grid environment are non-coordinated since application-level schedulers or brokers make scheduling decisions independently of the others in the system. Clearly, this can exacerbate the load sharing and utilization problems of distributed resources due to sub-optimal schedules that are likely to occur. To overcome these limitations, we propose a mechanism for coordinated sharing of distributed clusters based on computational economy. The resulting environment, called Grid-Federation, allows the transparent use of resources from the federation when local resources are insufficient to meet its users' requirements. The use of computational economy methodology in coordinating resource allocation not only facilitates the Quality of Service (QoS)-based scheduling, but also enhances utility delivered by resources. We show by simulation, while some users that are local to popular resources can experience higher cost and/or longer delays, the overall users' QoS demands across the federation are better met. Also, the federation's average case message-passing complexity is seen to be scalable, though some jobs in the system may lead to large numbers of messages before being scheduled.