Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost-time optimization algorithm

  • Authors:

  • Rajkumar Buyya;Manzur Murshed;David Abramson;Srikumar Venugopal

  • Affiliations:

  • Grid Computing and Distributed Systems Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville, Melbourne, Australia;Gippsland School of Computing and Information Technology, Monash University, Churchill, Australia;School of Computer Science and Software Engineering, Monash University, Melbourne, Australia;Grid Computing and Distributed Systems Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville, Melbourne, Australia

  • Venue:
  • Software—Practice & Experience
  • Year:
  • 2005

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Abstract

Computational Grids and peer-to-peer (P2P) networks enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. The management and composition of resources and services for scheduling applications, however, becomes a complex undertaking. We have proposed a computational economy framework for regulating the supply of and demand for resources and allocating them for applications based on the users' quality-of-service requirements. The framework requires economy-driven deadline-and budget-constrained (DBC) scheduling algorithms for allocating resources to application jobs in such a way that the users' requirements are met, In this paper, we propose a new scheduling algorithm, called the DBC cost-time optimization scheduling algorithm, that aims not only to optimize cost, but also time when possible. The performance of the cost-time optimization scheduling algorithm has been evaluated through extensive simulation and empirical studies for deploying parameter sweep applications on global Grids.