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This paper describes a methodology that provides detailed predictive performance information throughout the software design and implementation cycles. It is struc-tured around a hierarchy of performance models that describe the computing system in terms of its software, parallelization, and hardware components. The methodology is illustrated with an initial(More)
Scalable management and scheduling of dynamic grid resources requires new technologies to build the next generation intelligent grid environments. This work demonstrates that AI techniques can be utilised to achieve effective workload and resource management. A combination of intelligent agents and multi-agent approaches is applied to both local grid(More)
Performance prediction is set to play a significant role in supportive middleware that is designed to manage workload on parallel and distributed computing systems. This middleware underpins the discovery of available resources, the identification of a task's requirements and the match-making, scheduling and staging that follow. This paper documents two(More)
Grid middleware development has advanced rapidly over the past few years to support component-based programming models and service-orientated architectures. This is most evident with the forthcoming release of the Globus toolkit (GT4) which represents a convergence of concepts (and standards) from the web services community. Grid applications are(More)
—This paper addresses workload allocation techniques for two types of sequential jobs that might be found in multicluster systems, namely, non-real-time jobs and soft real-time jobs. Two workload allocation strategies, the Optimized mean Response Time (ORT) and the Optimized mean Miss Rate (OMR), are developed by establishing and numerically solving two(More)
This paper addresses the dynamic scheduling of parallel jobs with QoS demands (soft-deadlines) in multi-clusters and grids. Three metrics (over-deadline, makespan and idle-time) are combined with variable weights to evaluate the scheduling performance. These three metrics are used to measure the extent of the jobs' QoS demand compliance, the resource(More)
A new methodology is presented in this paper for resource management in a metacomputing environment using a hierarchy of homogeneous agents that has the capability of service discovery. The PACE [6] tools are used to provide quantitative data concerning the performance of sophisticated applications running on local high performance resources. At(More)
Before an application modelled as a Directed Acyclic Graph (DAG) is executed on a heterogeneous system, a DAG mapping policy is often enacted. After mapping, the tasks (in the DAG-based application) to be executed at each computational resource are determined. The tasks are then sent to the corresponding resources, where they are orchestrated in the(More)
Grid computing is becoming a mainstream technology for large-scale distributed resource sharing and system integration. Workflow management is emerging as one of the most important grid services. In this work, a workflow management system for grid computing, called GridFlow, is presented, including a user portal and services of both global grid workflow(More)
Resource management is an important infrastructure in the grid computing environment. Scalability and adaptability are two key challenges in the implementation of such complex software systems. In this work we introduce a new model for resource management in a metacomputing environment using a hierarchy of homogeneous agents that has the capability of(More)