Gerhard Engelbrecht

QoS support for time-critical grid workflow applications

Time critical grid applications as for example simulations for medical surgery or disaster recovery have special quality of service requirements. The Vienna Grid Environment, developed and evaluated in the context of the EU Project GEMSS, facilitates the provision of HPC applications as QoS-aware grid services by providing support for dynamic negotiation of various QoS guarantees like required execution time and price. In this paper, we extend the QoS mechanisms offered by the Vienna Grid Environment to workflow applications. We describe QoS extensions of the business process execution language and present a first prototype of a corresponding QoS-aware workflow engine which implements different strategies in order to bind the tasks of a workflow to adequate grid services subject to user-specified QoS constraints. We present different grid workflow planning approaches as well as first experimental results

VGE - a service-oriented grid environment for on-demand supercomputing

In this paper, we present the Vienna Grid Environment, a service-oriented grid infrastructure based on standard Web services technologies. VGE automates the provision of HPC applications as grid services for on-demand super-computing and simplifies the construction of client-side applications. As a key distinguishing feature, VGE supports a flexible QoS negotiation model which enables clients to negotiate dynamically, and on a case-by-case basis, QoS guarantees on execution time and price with potential service providers. The VGE service provision framework is currently utilized in the context of the EU Project GEMSS, which focuses on the provision of advanced medical simulation services by means of a grid infrastructure.

A Service-Oriented Grid Infrastructure for Biomedical Data and Compute Services

Service-oriented Grid technologies are increasingly utilized for the realization of future biomedical IT infrastructures since they offer unprecedented opportunities for the integration of advanced analysis and simulation applications as well as distributed heterogeneous data sources and information systems. The European Unions @neurIST project is developing a Grid-based IT infrastructure for the management of all processes linked to research, diagnosis, and treatment development for complex and multifactorial diseases encompassing data repositories, computational analysis services, and information systems handling multiscale, multimodal information at distributed sites. This paper provides an overview of the @neurIST Grid middleware and outlines the infrastructure offered for the provision of advanced compute and data services to support computationally demanding modeling and simulation tasks and to access heterogeneous distributed data sources through semantic integration.

A Generic QoS Infrastructure for Grid Web Services

QoS support to ensure timeliness of results is a key requirement for applications that rely on Grid services for computationally demanding tasks. In this paper we present a generic Grid infrastructure based on standard Web Services which provides flexible application-level QoS support, addressing the special requirements of time-critical Grid applications. We describe the provision of parallel applications as QoS-aware Grid services, which are capable of dynamically negotiating with clients QoS guarantees for response time and price in the form of Web Service Level Agreements. The presented techniques have been implemented within the Vienna Grid Environment and utilized in the context of the EU project GEMSS for the development of Grid-enabled medical simulation services.

@neurIST: Infrastructure for Advanced Disease Management Through Integration of Heterogeneous Data, Computing, and Complex Processing Services

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the systems architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the systems ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

@neurIST - Towards a System Architecture for Advanced Disease Management through Integration of Heterogeneous Data, Computing, and Complex Processing Services

This paper presents the system architecture of the @neurIST project, which aims at supporting the research and treatment of cerebral aneurysms by bringing together heterogeneous data, computing and complex processing services. The architecture is generic enough to adapt it to the treatment of other diseases beyond cerebral aneurysms. The paper describes the generic requirements of the system and presents the architecture, applications and middleware technologies used to realise the system and highlights the innovations in @neurIST.

Towards quality of service support for grid workflows

Service-oriented workflow languages are being considered as a key programming paradigm for composing Grid applications from basic services. In this paper we present QoS support for grid workflows addressing the special requirements of time-critical Grid applications, as for example medical simulation services. QoS support for grid workflow is currently being developed in the context of the Vienna Grid Environment, a service-oriented Grid infrastructure for the provision of parallel simulation codes as QoS-aware Grid services, which are capable of dynamically negotiating with clients various QoS guarantees, e.g. with respect to execution time and price. We use a real world Grid service for medical image reconstruction to describe the main features of our approach and present the first prototype of a QoS-aware workflow engine which supports dynamic QoS negotiation and QoS-aware workflow execution.

Quality of Service Negotiation for Commercial Medical Grid Services

The GEMSS project has developed a service-oriented Grid that supports the provision of medical simulation services by service providers to clients such as hospitals. We outline the GEMSS architecture, legal framework and the security features that characterise the GEMSS infrastructure. High levels of quality of service are required and we describe a reservation-based approach to quality of service, employing a quality of service management system that iteratively finds suitable reservations and uses application specific performance models. The GEMSS Grid is a commercial environment so we support flexible pricing models and a FIPA reverse English auction protocol. Signed Web Service Level Agreement contracts are exchanged to commit parties to a quality of service agreement before job execution occurs. We run four experiments across European countries using high performance computing resources running advanced resource reservation schedulers. These experiments provide evidence for our Grid’s rational behaviour, both at the level of service provider quality of service management and at the higher level of the client choosing between competing service providers. The results lend support to our economic model and the technology we use for our medical application domain.

Prediction of cerebral aneurysm rupture using hemodynamic, morphologic and clinical features: a data mining approach

Cerebral aneurysms pose a major clinical threat and the current practice upon diagnosis is a complex, lengthy, and costly, multicriteria analysis, which to date is not fully understood. This paper reports the development of several classifiers predicting whether a given clinical case is likely to rupture taking into account available information of the patient and characteristics of the aneurysm. The dataset used included 157 cases, with 294 features each. The broad range of features include basic demographics and clinical information, morphological characteristics computed from the patients medical images, as well as results gained from personalised blood flow simulations. In this premiere attempt the wealth of aneurysm-related information gained from multiple heterogeneous sources and complex simulation processes is used to systematically apply different data-mining algorithms and assess their predictive accuracy in this domain. The promising results show up to 95% classification accuracy. Moreover, the analysis also enables to confirm or reject risk factors commonly accepted or suspected in the domain.

Towards collaborative data management in the VPH-Share project

The goal of the Virtual Physiological Human Initiative is to provide a systematic framework for understanding physiological processes in the human body in terms of anatomical structure and biophysical mechanisms across multiple length and time scales. In the long term it will transform the delivery of European healthcare into a more personalised, predictive, and integrative process, with significant impact on healthcare and on disease prevention. This paper outlines how the recently funded project VPH-Share contributes to this vision. The project is motivated by the needs of the whole VPH community to harness ICT technology to improve health services for the individual. VPH-Share will provide the organisational fabric (the infostructure), realised as a series of services, offered in an integrated framework, to expose and to manage data, information and tools, to enable the composition and operation of new VPH workflows and to facilitate collaborations between the members of the VPH community.