Abstract not found

The Computational Grid is a promising platform for the efficient execution of parameter sweep applications over large parameter spaces. To achieve performance on the Grid, such applications must be scheduled so that shared data files are strategically placed to maximize reuse, and so that the application execution can adapt to the deliverable performance potential of target heterogeneous, distributed and shared resources. Parameter sweep applications are an important class of applications and would greatly benefit from the development of Grid middleware that embeds a scheduler for performance and targets Grid resources transparently. In this paper we describe a user-level Grid middleware project, the AppLeS Parameter Sweep Template (APST), that uses application-level scheduling techniques [1] and various Grid technologies to allow the efficient deployment of parameter sweep applications over the Grid. We discuss...

The Computational Grid provides a promising platform for the efficient execution of parameter sweep applications over very large parameter spaces. Scheduling such applications is challenging because target resources are heterogeneous, because their load and availability varies dynamically, and because independent tasks may share common data files. In this paper, we propose an adaptive scheduling algorithm for parameter sweep applications on the Grid. We modify standard heuristics for task/host assignment in perfectly predictable environments (Max-min, Min-min, Sufferage), and we propose an extension of Sufferage called XSufferage. Using simulation, we demonstrate that XSufferage can take advantage of file sharing to achieve better performance than the other heuristics. We also study the impact of inaccurate performance prediction on scheduling. Our study shows that: (i) different heuristics behave differently when predictions are inaccurate; (ii) increased adaptivity leads to better performance.

In high energy physics, bioinformatics, and other disciplines, we encounter applications involving numerous, loosely coupled jobs that both access and generate large data sets. Socalled Data Grids seek to harness geographically distributed resources for such large-scale data-intensive problems. Yet effective scheduling in such environments is challenging, due to a need to address a variety of metrics and constraints (e.g., resource utilization, response time, global and local allocation policies) while dealing with multiple, potentially independent sources of jobs and a large number of storage, compute, and network resources.

We advocate a novel approach to grid computing that is based on a combination of "classic" operating system level virtual machines (VMs) and middleware mechanisms to manage VMs in a distributed environment. The abstraction is that of dynamically instantiated and mobile VMs that are a combination of traditional OS processes (the VM monitors) and files (the VM state). We give qualitative arguments that justify our approach in terms of security, isolation, customization, legacy support and resource control, and we show quantitative results that demonstrate the feasibility of our approach from a performance perspective. Finally, we describe the middleware challenges implied by the approach and an architecture for grid computing using virtual machines.

Ensembles of distributed, heterogeneous resources, also known as Computational Grids are emerging as critical platforms for high-performance and resource-intensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, secondary storage, and other resources during a single execution. However, achieving this performance potential in dynamic, heterogeneous environments is challenging. Recent experience with distributed applications indicates that adaptivity is fundamental to achieving application performance in dynamic Grid environments. The AppLeS (Application Level Scheduling) project provides a methodology, application software, and software environments for adaptively scheduling and deploying applications in dynamic, heterogeneous, multi-user Grid environments. In this paper, we discuss the AppLeS project and outline our results.

Access to remote data is one of the principal challenges of grid computing. While performing I/O, grid applications must be prepared for server crashes, performance variations, and exhausted resources. To achieve high throughput in such a hostile environment, applications need a resilient service that moves data while hiding errors and latencies. We illustrate this idea with Kangaroo, a simple data movement system that makes opportunistic use of disks and networks to keep applications running. We demonstrate that Kangaroo can achieve better end-to-end performance than traditional data movement techniques, even though its individual components do not achieve high performance.

In this paper we report on preliminary work and architectural design carried out in the "Data Management" work package in the International Data Grid project. Our aim within a time scale of three years is to provide Grid middleware services supporting the I/Ointensive world-wide distributed next generation experiments in HighEnergy Physics, Earth Observation and Bioinformatics. The goal is to specify, develop, integrate and test tools and middleware infrastructure to coherently manage and share Petabyte-range information volumes in high-throughput production-quality Grid environments. The middleware will allow secure access to massive amounts of data in a universal namespace, to move and replicate data at high speed from one geographical site to another, and to manage synchronisation of remote copies. We put much attention on clearly specifying and categorising existing work on the Grid, especially in data management in Grid related projects.

The next generation of scientific experiments and studies, popularly called e-Science, is carried out by large collaborations of researchers distributed around the world engaged in analysis of huge collections of data generated by scientific instruments. Grid computing has emerged as an enabler for e-Science as it permits the creation of virtual organizations that bring together communities with common objectives. Within a community, data collections are stored or replicated on distributed resources to enhance storage capability or efficiency of access. In such an environment, scientists need to have the ability to carry out their studies by transparently accessing distributed data and computational resources. In this paper, we propose and develop a Grid broker that mediates access to distributed resources by (a) discovering suitable data sources for a given analysis scenario, (b) suitable computational resources, (c) optimally mapping analysis jobs to resources, (d) deploying and monitoring job execution on selected resources, (e) accessing data from local or remote data source during job execution and (f) collating and presenting results. The broker supports a declarative and dynamic parametric programming model for creating grid applications. We have used this model in grid-enabling a high energy physics analysis application (Belle Analysis Software Framework). The broker has been used in deploying Belle experiment data analysis jobs on a grid testbed, called Belle Analysis Data Grid, having resources distributed across Australia interconnected through GrangeNet.

The next generation of scientific experiments and studies, popularly called as e-Science, is carried out by large collaborations of researchers distributed around the world engaged in analysis of huge collections of data generated by scientific instruments. Grid computing has emerged as an enabler for e-Science as it permits the creation of virtual organizations that bring together communities with common objectives. Within a community, data collections are stored or replicated on distributed resources to enhance storage capability or efficiency of access. In such an environment, scientists need to have the ability to carry out their studies by transparently accessing distributed data and computational resources. In this paper, we propose and develop a Grid broker that mediates access to distributed resources by (a) discovering suitable data sources for a given analysis scenario, (b) suitable computational resources, (c) optimally mapping analysis jobs to resources, (d) deploying and monitoring job execution on selected resources, (e) accessing data from local or remote data source during job execution and (f) collating and presenting results. The broker supports a declarative and dynamic parametric programming model for creating grid applications. We have used this model in grid-enabling a high energy physics analysis application (Belle Analysis Software Framework). The broker has been used in deploying Belle experiment data analysis jobs on a grid testbed, called Belle Analysis Data Grid, having resources distributed across Australia interconnected through GrangeNet.