A unified resource scheduling framework for heterogeneous computing environments

A major challenge in metacomputing systems (computational grids) is to effectively use their shared resources, such as compute cycles, memory, communication network, and data repositories, to optimize desired global objectives. We develop a unified framework for resource scheduling in metacomputing systems where tasks with various requirements are submitted from participant sites. Our goal is to minimize the overall execution time of a collection of application tasks. In our model, each application task is represented by a directed acyclic graph (DAG). A task consists of several subtasks and the resource requirements are specified at subtask level. Our framework is general and it accommodates emerging notions of quality of service (QoS) and advance resource reservations. We present several scheduling algorithms which consider compute resources and data repositories that have advance reservations. As shown by our simulation results, it is advantageous to schedule the system resource separately. Our algorithms have at least 30% improvement over the separated approach with respect to completion time.