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Today, many scientific disciplines heavily rely on computer systems for in-silico experimentation or data management and analysis. The employed computer hard- and software is heterogeneous and complies to different standards, interfaces and protocols for interoperation. Grid middleware systems like UNICORE 6 try to hide some of the complexity of the underlying systems by offering high-level, uniform...
Scientific workflows generally involve the distribution of tasks to distributed resources, which may exist in different administrative domains. The use of distributed resources in this way may lead to faults, and detecting them, identifying them and subsequently correcting them remains an important research challenge. We introduce a fault taxonomy for scientific workflows that may help in conducting...
Porting applications to Distributed Computing Infrastructures (DCIs) is eased by the use of workflow abstractions. Yet, estimating the impact of the execution DCI on application performance is difficult due to the heterogeneity of the resources available, middleware and operation models. This paper describes a workflow-based experimental method to acquire objective performance comparison criterions...
Workflow management systems allow the development of complex applications at a higher level, by orchestrating functional components without handling the implementation details. Although a wide range of workflow engines are developed in enterprise environments, the open source engines available for scientific applications lack some functionalities or are too difficult to use for non-specialists. Moreover,...
Currently, most workflow management systems in Grid environments provide push-oriented job distribution strategies, where jobs are explicitly delegated to resources. In those scenarios the dedicated resources execute submitted jobs according to the request of a workflow engine or Grid wide scheduler. This approach has various limitations, particularly if human interactions should be integrated in...
Facing computing and data intensive problems scientists are forced to use all the available resources to solve their problems. This applies to the usage of different distributed e-Infrastructures based on different concepts and architectures. Providing the end user with easy access to such complex systems is a very challenging task and refers to the issues related to the interoperability and interoperation...
This paper presents the design, implementation, and usage of a virtual laboratory for medical image analysis. It is fully based on the Dutch grid, which is part of the Enabling Grids for E-sciencE (EGEE) production infrastructure and driven by the gLite middleware. The adopted service-oriented architecture enables decoupling the user-friendly clients running on the user's workstation from the complexity...
Grid workflow authoring tools are typically specific to particular workflow engines built into grid middleware, or are application specific and are designed to interact with specific software implementations. g-Eclipse is a middleware independent grid workbench that aims to provide a unified abstraction of the grid and includes a grid workflow builder to allow users to author and deploy workflows...
Neuroimaging research is increasingly shifting towards distributed computing architectures for the processing of ever growing neuroimaging datasets. At present compute and data intensive neuroimaging workflows often use cluster-based resources to analyse datasets. For increased scalability however, distributed grid-based analysis platforms may be required. Such an analysis infrastructure necessitates...
Workflow management systems (WMS) in Grid computing are becoming more important to solve the sophisticated problem such as genomic analysis, drug discovery, disease identification, etc. In this paper, we discuss how to guarantee different user requirements according to user SLA in Grid workflow management system. An adaptive workflow management system for Quality of Service (QoS) constrained medical...
As science technology grows, medical application is becoming more complex to solve the physiological problems within expected time. Workflow management systems (WMS) in Grid computing are promising solution to solve the sophisticated problem such as genomic analysis, drug discovery, disease identification, etc. Although existing WMS can provide basic management functionality in Grid environment, consideration...
In this paper we present improvement of the performance of Grid Direct Acyclic Graph (DAG) workflow genetic algorithm by harnessing the power of High Level Petri-Nets workflow model. Genetic Algorithms are very powerful optimization technique that is easily parallelized using different approaches which makes it ideal for the Grid. The High Level Petri-Net workflow model greatly outperforms currently...
The main purpose of the research work presented in this paper was to model and implement a workflow based architecture for Web service composition which allows the integration of composed Web services in a Grid middleware. The composed Web services are integrated as computational nodes in a workflow description and execution platform. To prove the presented theoretical concepts, a prototype was also...
As computer and network technology grows, medical application is become more complex to solve the physiological problems within expected time. Workflow management systems (WMS) in grid computing are becoming more important to solve the sophisticated problem such as genomic analysis, drug discovery, disease identification, etc. Although existing WMS can provide basic management functionality in grid...
Resource-intensive and complex medical imaging applications can benefit from the use of scientific workflow technology for their design, rapid implementation and reuse, but at the same time they require a grid computing infrastructure to execute efficiently. In this paper we describe a technical architecture that bridges the gap between the Taverna workflow management system and the EGEE grid infrastructure...
In recent years the power of Grid computing has grown exponentially through the development of advanced middleware systems. While usage has increased, the penetration of Grid computing in the scientific community has been less than expected by some. This is due to a steep learning curve and high entry barrier that limit the use of Grid computing and advanced cyberinfrastructure. In order for the scientists...
From personal software to advanced systems, caching mechanisms have steadfastly been a ubiquitous means for reducing workloads. It is no surprise, then, that under the grid and cluster paradigms, middlewares and other large-scale applications often seek caching solutions. Among these distributed applications, scientific workflow management systems have gained ground towards mitigating the often painstaking...
Workflow execution time predictions for Grid infrastructures is of critical importance for optimized workflow executions, advance reservations of resources, and overhead analysis. Predicting workflow execution time is complex due to multeity of workflow structures, involvement of several Grid resources in workflow execution, complex dependencies of workflow activities and dynamic behavior of the Grid...
BPEL is the de facto standard for business process modeling in today's enterprises and is a promising candidate for the integration of business and Grid applications. Current BPEL implementations do not provide mechanisms to schedule service calls with respect to the load of the target hosts. In this paper, a solution that automatically schedules workflow steps to underutilized hosts and provides...
A large-scale simulation in e-science experiments can be modeled by using a workflow. The ProGenGrid workflow management system is being developed at the University of Salento in Lecce since 2004 and consists of an editor for designing the experiment and an engine for scheduling the jobs in a computational grid. The initial version was based on wrapping the bioinformatics tools as Web services and...
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