This article describes an approach for predicting the run-time of jobs using a technique that works in three phases. Each one is independently adjusting to a user's behaviour in order to lead to accurate forecasts. In heterogeneous and distributed environments it is necessary to create schedules for utilizing the resources in an efficient way, but the generation of these schedules often poses a problem for a scheduler, as it has to incorporate several aspects like priorities, system load, Service Level Agreements. One possibility to support a scheduler in doing its work is to provide accurate predictions of the run-times of the submitted jobs.A large number of current techniques for run-time prediction offer statistical models - in the majority of cases linear ones - that are deployed on previously filtered data. As users have different jobs due to their field of work, and the attributes of their jobs differ, because of the different requirements they have, filtering data and choosing an appropriate method for a forecast has to cover these aspects. Motivated by this we propose an adaptive prediction system, where in each one of the phases we adjust our methodology on basis of the former behaviour of a user. This leads to a user specific clustering of data and to a flexible utilization of different prediction techniques in order to create a user-centred prediction model.

In this article we describe an approach for the prediction of the run-time of jobs in heterogeneous environments that applies a meta-prediction algorithm working in multiple phases. For an efficient utilization of hardware resources, it is necessary to support schedulers with detailed information about the jobs that are going to be dispatched. One technique is to provide accurate forecasts of application run-times. A large number of current approaches focus on limited sets of prediction techniques (often linear ones) whereas most times only one is deployed on a dataset, ignoring the different characteristics that are "denoted" inherently by the data and therefore are not obvious. For that reason we are proposing an adaptive system for run-time prediction that offers a large set of different and differently parameterized predictors respectively, whereas only the appropriate prediction techniques for specifically filtered clusters of jobs are executed.

In a distributed environment, scientists using clusters, supercomputers, and the grid, may often find themselves in a place apart from the computing facilities. Therefore, controlling and steering interactive applications executing remotely requires a transport mechanism connecting the scientist's workstation with the remote computer. The concept presented in this paper only needs minimal prerequisites to fulfil this task. On the user side, a Web browser executing a Java applet is sufficient, on the remote computer, a Web server able to start the remote application is required. The "connecting bundling'' method presented herein allows demanding remote interactive applications to be controlled even in a restrictive network environment.

This article describes a system for run-time prediction of applications in heterogeneous environments. To exploit the power of computational grids, scheduling systems need profound information about the job to be executed. The run-time of a job is - beside others - not only dependent of its kind and complexity but also of the adequacy and load of the remote host where it will be executed. Accounting and billing are additional aspects that have to be considered when creating a schedule. Currently predictions are achieved by using descriptive models of the applications or by applying statistical methods to former jobs mostly neglecting the behaviour of users. Motivated by this, we propose a method that is not only based on the characteristics of a job but also takes the behaviour of single users and groups of similar users respectively into account. The basic idea of our approach is to cluster users, hosts and jobs and apply multiple methods in order to detect similarities and create forecasts. This is achieved by tagging jobs with attributes and by deriving predictions for similar attributed jobs whereas the recent behaviour of a user determines which predictions are finally taken.

This paper describes the "fusion plasma application" which is developed within the "Interactive European Grid" (int.eu.grid), making use of its architecture to produce interactive visualisation of a distributed simulation executing on the grid. In properly combining and, where necessary, adopting the building blocks "migrating desktop" (MD), "roaming access server" (RAS), "grid visualisation tool" (GVid) and "grid interactive tool" (glogin), the int.eu.grid adds extensions for interactive visualisation to grid middleware in a novel way.

Summary form only given. GRIDs are large-scale distributed computing infrastructures that enable the integrated and collaborative use of high-end computers, networks, databases, and scientific instruments owned and managed by multiple organizations. Currently, a lot of national and international grid projects aim in building and using grids. One of them is the Austrian Grid. This project targets different areas of applications, including medical sciences, high-energy physics, applied numerical simulations, astrophysical simulations and solar observations, as well as meteorology and geophysics. Corresponding to the previous experience of the initiators of the project a lot of the applications are parallel ones. In the first part of the talk an overview on Austrian Grid will be presented. We will speak about the goals and the results in general. In the second part of the lecture the focus will be on parallel applications. We will discuss some answers given by the research of Austrian grid to questions like: how to map parallel applications onto a grid, which kind of tools are necessary, which extensions of standard middleware are desirable

While frameworks and application programming interfaces for virtual reality are commonplace today, designing scenarios for virtual environments still remains a tedious and time consuming task. We present a new authoring tool which combines scene assembly and visual programming in a desktop application with instant testing, tuning and planning in an immersive virtual environment. Two authors can work together, one with the desktop authoring application and the other in the immersive VR-simulation, to build a complete scenario.

Examining a program's state during execution is of major importance for software developers when analyzing an application for performance tuning or error detection. This demand for suitable analysis tools is further pushed by parallel and distributed programs, which process huge amounts of complex data. A solution to this problem is offered by visualization, such that even a large amount of data can be presented efficiently for the user's investigations. This paper presents an immersive approach, where monitoring and steering activities combined with virtual reality visualization components help the user to understand the behavior of running parallel and distributed programs. On the one hand, program visualization tries to induce a "feeling" for the program. On the other hand, data visualization provides a facility to display preliminary results of any kind of scientific calculations.