Examples of Grids

A.5.1Industrial Grids

A.5.1.1DAME (Jim Austin, York University)

The theme of DAME project is the design and implementation of a fault diagnosis and prognosis system based on the Grid computing paradigm and the deployment of Grid services. In particular DAME focuses on developing an improved computer-based fault diagnosis and prognostic capability and integrating that capability with a predictive maintenance system in the context of aero-engine maintenance. The term “predictive maintenance” implies that there is sufficient time interval between the detection of a behaviour that departs from normal and the actual occurrence of a failure. The DAME system will deploy Grid services within this time window to develop a diagnosis of why an engine has deviated from normal behaviour, to provide prognosis (understanding what will happen) and to plan remedial actions that may be taken a safe and convenient point when the impact of maintenance is minimized. A central challenge of the project is to develop a proof of concept demonstrator that will address the commercial and technical requirements of the industrial partners within the consortium (Rolls-Royce and Data Systems & Solutions). The commercial considerations of the project create a set of demanding technical requirements which will be addressed through Grid computing techniques. These include:

Transmission of high volumes of data from remote data repositories, to and from the maintenance points; Access to highly specialised data analysis and diagnosis software services;

Necessity to search extremely large volumes of data.

Virtual Organisations (VO) where the various members are located in various parts of the world and where complex interactions among multiple agents or stakeholders are needed and are facilitated by connection through the Grid

The need to provide supporting or qualifying evidence for the diagnosis or prognosis offered;

Addressing the business critical aspects of the commercial deployment, developing a Grid enabled system that can meet stringent dependability requirements, including Quality of Service and Security issues.

A.5.1.2Geodise (Simon Cox, Southampton University)

Engineering design search and optimisation is the process whereby engineering modelling and analysis are exploited to yield improved designs. Intelligent search tools will become a vital component of all engineering design systems and will steer the user through the process of setting up, executing and post-processing design search and optimisation activities. Such systems typically require large-scale distributed simulations to be coupled with tools to describe and modify designs using information from a knowledge base. These tools are usually physically distributed and under the control of multiple elements in the supply chain. Whilst evaluation of a single design may require the analysis of gigabytes of data, to improve the process of design can require assimilation of terabytes of distributed data. Achieving the latter goal will lead to the development of intelligent search tools.

The focus of the Geodise project is on the use of computational fluid dynamics with BAE Systems, Rolls Royce, and Fluent (world leading developers of CFD codes). Geodise is being developed by the Universities of Southampton, Oxford and Manchester in collaboration with other industrial partners working in the domains of hardware (Intel), software (Microsoft), systems integration (Compusys), knowledge technologies (Epistemics), and grid-middleware (Condor).

In summary, design optimisation needs integrated services

• Design improvements driven by CAD tools coupled to advanced analysis codes (CFD, FEA, CEM etc.)

• On demand heterogeneous distributed computing and data spread across companies and time zones.

• Optimization “for the masses” alongside manual search as part of a problem solving environment.

• Knowledge based tools for advice and control of process as well as product.

Geodise will provide grid-based seamless access to an intelligent knowledge repository, a state-of-the-art collection of optimisation and search tools, industrial strength analysis codes, and distributed computing and data resources.

APPLICATION SERVICE PROVIDER COMPUTATION GEODISE PORTAL OPTIMISATION Engineer Parallel machines Clusters Internet Resource Providers

Pay-per-use Optimisation archive Intelligent Application Manager Intelligent Resource Provider Licenses and code Session database Design archive OPTIONS System Knowledge repository Traceability Visualization

Globus, Condor, OGSA

Ontology for Engineering, Computation, & Optimisation and Design Search CAD System CADDS IDEAS ProE CATIA, ICAD Analysis CFD FEM CEM Reliability Security QoS

A.5.2Military and Defense Grids (Mike Kirton, QinetiQ)

Information Superiority and Decision Dominance are at the heart of new military thinking about the conduct of modern warfare. For example, Network-Centric Warfare (http://www.c3i.osd.mil/NCW) “derivesits power from the effective linking or networking of the warfightingenterprise.” Joint Vision 2020 (http://www.dtic.mil/jointvision) emphasises the importance of collecting, processing and disseminating an uninterrupted flow of information while exploiting or denying an adversary’s ability to do the same. The UK has recently announced its Network Enabled Capability initiative with the aim of enhancing military capability through the better exploitation of information across the battlespace. As recent world events have shown, multi-national coalitions are playing an increasingly important role in military operations. Indeed, military coalitions are archetypal dynamic virtual organisations that have a limited lifetime, are formed from heterogeneous ‘come as you are’ elements at short notice, and need secure and partial sharing of information.

A common requirement across these programmes is the need to inter-operate and integrate heterogeneous distributed systems and to work with large volumes of information and high data rates. In these respects, they could benefit substantially from Grid computing concepts. However, security, resilience, flexibility and cost effectiveness are key considerations for the deployment of military Grids. It is also likely that there will be the need for multiple Grids supporting different aspects of the military enterprise, e.g. ‘heavyweight’ Grids for imagery data and ‘lightweight’ ubiquitous Grids running on the PDAs of military commanders in a headquartersthese Grids will need to be interoperable.

Currently, there are a number of US military programmes exploring Grid technologies in the context of Network- Centric Warfare, for example Joint Battlespace Infosphere (http://www.rl.af.mil/programs/jbi/default.cfm), Expeditionary Sensor Grid (http://www.nwdc.navy.mil/OperationsHome/CNAN.asp) and the Fleet Battle Experiments (http://program38.nrl.navy.mil/fbe.htm). In addition, the Coalition Agents Experiment (http://www.aiai.ed.ac.uk/project/coax) demonstrated how an agent-based Grid infrastructure could support the construction of a coherent command support system for coalition operations.