Abstract: Computational Grids, coupling geographically distributed resources such as PCs, workstations, clusters, and scientific instruments, have emerged as a next generation computing platform for solving large-scale problems in science, engineering, and commerce. However, application development, resource management, and scheduling in these environments continue to be a complex undertaking. In this article, we discuss our efforts in developing a resource management system for scheduling computations on resources distributed across the world with varying quality of service. Our service-oriented grid computing system called Nimrod-G manages all operations associated with remote execution including resource discovery, trading, scheduling based on economic principles and a user defined quality of service requirement. The Nimrod-G resource broker is implemented by leveraging existing technologies such as Globus, and provides new services that are essential for constructing industrial-strength Grids. We discuss results of preliminary experiments on scheduling some parametric computations using the Nimrod-G resource broker on a world-wide grid testbed that spans five continents.
1. Introduction
The accelerated development of Grid computing systems has positioned them as promising next generation computing platforms. They enable the coordinated use of geographically distributed resources, often owned by autonomous organizations, for creating virtual enterprises for solving large-scale problems in science, engineering, and commerce [1][7]. However, application composition, resource management and scheduling in these environments is a complex undertaking. This is due to the geographic distribution of resources that are often owned by different organizations having different usage policies and cost models, and varying loads and availability patterns. To address these resource management challenges, we have proposed and developed a computational economy framework for resource allocation and regulation of supply and demand for resources. The new framework offers incentive to resource owners for being part of the Grid and motivates resource users to trade off between time for results delivery and economic cost, i.e., deadline and budget [5].