Economic-based Distributed Resource Management and Scheduling for Grid Computing

Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. As the resources in the Grid are heterogeneous and geographically distributed with varying availability and a variety of usage and cost policies for diverse users at different times and, priorities as well as goals that vary with time. The management of resources and application scheduling in such a large and distributed environment is a complex task. This thesis proposes a distributed computational economy as an effective metaphor for the management of resources and application scheduling. It proposes an architectural framework that supports resource trading and quality of services based scheduling. It enables the regulation of supply and demand for resources and provides an incentive for resource owners for participating in the Grid and motives the users to trade-off between the deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for peer-to-peer distributed computing by developing users' quality-of-service requirements driven scheduling strategies and algorithms. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep applications

Application-Aware Scheduling of a Magnetohydrodynamics Application in the Legion Metasystem

Computational Grids have become an important and popular computing platform for both scientific and commercial distributed computing communities. However, users of such systems typically find achievement of application execution performance remains challenging. Although Grid infrastructures such as Legion and Globus provide basic resource selection functionality, work allocation functionality, and scheduling mechanisms, applications must interpret system performance information in terms of their own requirements in order to develop performance-efficient schedules. We describe a new high-performance scheduler that incorporates dynamic system information, application requirements, and a detailed performance model in order to create performance efficient schedules. While the scheduler is designed to provide improved performance for a magneto hydrodynamics simulation in the Legion Computational Grid infrastructure, the design is generalizable to other systems and other data-parallel, itera..