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. Copyright c 2005 John Wiley & Sons, Ltd.