Grid technology is widely emerging. Still, there is an eminent shortage of real Grid users, mostly due to the lack of a "critical mass" of widely deployed and reliable higher-level Grid services, tailored to application needs. The GridLab project aims to provide fundamentally new capabilities for applications to exploit the power of Grid computing, thus bridging the gap between application needs and existing Grid middleware. We present an overview of GridLab, a large-scale, EU-funded Grid project spanning over a dozen groups in Europe and the US. We first outline our vision of Grid-empowered applications and then discuss GridLab's general architecture and its Grid Application Toolkit (GAT). We illustrate how applications can be Grid-enabled with the GAT and discuss GridLab's scheduler as an example of GAT services.

This paper outlines the major components and function of the technologically integrated oncosimulator developed primarily within the Advancing Clinico Genomic Trials on Cancer (ACGT) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity-discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid, and the visualization of the predictions. A refining scenario for the eventual coupling of the oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.

Grid computing has become one of the most important research topics that appeared in the field of computing in the last years. Simultaneously, we have noticed the growing popularity of new Web-based technologies which allow us to create application-oriented Grid middleware services providing capabilities required for dynamic resource and job management, monitoring, security, etc. Consequently, end users are able to get easier access to geographically distributed resources. In this paper we present the results of our experiments with the Grid(Lab) Resource Management System (GRMS), which acts on behalf of end users and controls their computations efficiently using distributed heterogeneous resources. We show how resource matching techniques used within GRMS can be improved by the use of a job migration based rescheduling policy. The main aim of this policy is to shorten job pending times and reduce machine overloads. The influence of this method on application performance and resource utilization is studied in detail and compared with two other simple policies.

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