Satoshi Shirasuna

Building web services for scientific grid applications

Web service architectures have gained popularity in recent years within the scientific grid research community. One reason for this is that web services allow software and services from various organizations to be combined easily to provide integrated and distributed applications. However, most applications developed and used by scientific communities are not web-service-oriented, and there is a growing need to integrate them into grid applications based on service-oriented architectures. In this paper, we describe a framework that allows scientists to provide a web service interface to their existing applications as web services without having to write extra code or modify their applications in any way. In addition, application providers do not need to be experts in web services standards, such as Web Services Description Language, Web Services Addressing, Web Services Security, or secure authorization, because the framework automatically generates these details. The framework also enables users to discover these application services, interact with them, and compose scientific workflows from the convenience of a grid portal.

Performance comparison of security mechanisms for grid services

Security is one of the most important features for grid services. There are several specifications used to add security to grid services, and some of them have been implemented and are in use. However, since most of the security mechanisms involve slow XML manipulations, adding security to grid services introduces a big performance penalty. In this paper, we introduce various security mechanisms and compare their features and performance. Our evaluation shows that transport level security (SSL) is faster than message level security, and should be used if there is no special requirement to use message level security. For message level security, WS-SecureConversation is generally fast, but has a scalability problem.

Building Grid Portal Applications From a Web Service Component Architecture

This work describes an approach to building Grid applications based on the premise that users who wish to access and run these applications prefer to do so without becoming experts on Grid technology. We describe an application architecture based on wrapping user applications and application workflows as Web services and Web service resources. These services are visible to the users and to resource providers through a family of Grid portal components that can be used to configure, launch, and monitor complex applications in the scientific language of the end user. The applications in this model are instantiated by an application factory service. The layered design of the architecture makes it possible for an expert to configure an application factory service with a custom user interface client that may be dynamically loaded into the portal.

Dynamic, Adaptive Workflows for Mesoscale Meteorology

The Linked Environments for Atmospheric Discovery (LEAD) [122] is a National Science Foundation funded1 project to change the paradigm for mesoscale weather prediction from one of static, fixed-schedule computational forecasts to one that is adaptive and driven by weather events. It is a collaboration of eight institutions,2 led by Kelvin Droegemeier of the University of Oklahoma, with the goal of enabling far more accurate and timely predictions of tornadoes and hurricanes than previously considered possible. The traditional approach to weather prediction is a four-phase activity. In the first phase, data from sensors are collected. The sensors include ground instruments such as humidity and temperature detectors, and lightning strike detectors and atmospheric measurements taken from balloons, commercial aircraft, radars, and satellites. The second phase is data assimilation, in which the gathered data are merged together into a set of consistent initial and boundary conditions for a large simulation. The third phase is the weather prediction, which applies numerical equations to measured conditions in order to project future weather conditions. The final phase is the generation of visual images of the processed data products that are analyzed to make predictions. Each phase of activity is performed by one or more application components.

Programming Paradigms for Scientific Problem Solving Environments

Scientific problem solving environments (PSEs) are software platforms that allow a community of scientific users the ability to easily solve computational problems within a specific domain. They are designed to hide the details of general purpose programming by allowing the problem to be expressed, as much as possible, in the scientific language of the discipline. In many areas of science, the nature of computational problems has evolved from simple desktop calculations to complex, multidisciplinary activities that require the monitoring and analysis of remote data streams, database and web search and large ensembles of supercomputer-hosted simulations. In this paper we will look at the class of PSE that have evolved for these “Grid based” systems and we will consider the associated programming models they support. It will be argued that a hybrid of three standard models provides the right programming support to handle the majority of the applications of these PSEs.