Job Monitoring in an Interactive Grid Analysis Environment

The grid is emerging as a great computational resource but its dynamic behavior makes the Grid environment unpredictable. Systems and networks can fail, and the introduction of more users can result in resource starvation. Once a job has been submitted for execution on the grid, monitoring becomes essential for a user to see that the job is completed in an efficient way, and to detect any problems that occur while the job is running. In current environments once a user submits a job he loses direct control over the job and the system behaves like a batch system: the user submits the job and later gets a result back. The only information a user can obtain about a job is whether it is scheduled, running, cancelled or finished. Today users are becoming increasingly interested in such analysis grid environments in which they can check the progress of the job, obtain intermediate results, terminate the job based on the progress of job or intermediate results, steer the job to other nodes to achieve better performance and check the resources consumed by the job. In order to fulfill their requirements of interactivity a mechanism is needed that can provide the user with real time access to information about different attributes of a job. In this paper we present the design of a Job Monitoring Service, a web service that will provide interactive remote job monitoring by allowing users to access different attributes of a job once it has been submitted to the interactive Grid Analysis Environment

Számítóháló alkalmazások teljesítményanalízise és optimalizációja = Performance analysis and optimisation of grid applications

Számítóhálón (griden) futó alkalmazások, elsősorban workflow-k hatékony végrehajtására kerestünk újszerű megoldásokat a grid teljesítményanalízis és optimalizáció területén. Elkészítettük a Mercury monitort a grid teljesítményanalízis követelményeit figyelembe véve. A párhuzamos programok monitorozására alkalmas GRM monitort integráltuk a relációs adatmodell alapú R-GMA grid információs rendszerrel, illetve a Mercury monitorral. Elkészült a Pulse, és a Prove vizualizációs eszköz grid teljesítményanalízist támogató verziója. Elkészítettünk egy state-of-the-art felmérést grid teljesítményanalízis eszközökről. Kidolgoztuk a P-GRADE rendszer workflow absztrakciós rétegét, melyhez kapcsolódóan elkészült a P-GRADE portál. Ennek segítségével a felhasználók egy web böngészőn keresztül szerkeszthetnek és hajthatnak végre workflow alkalmazásokat számítóhálón. A portál különböző számítóháló implementációkat támogat. Lehetőséget biztosít információ gyűjtésére teljesítményanalízis céljából. Megvizsgáltuk a portál erőforrás brókerekkel való együttműködését, felkészítettük a portált a sikertelen futások javítására. A végrehajtás optimalizálása megkövetelheti az alkalmazás egyes részeinek áthelyezését más erőforrásokra. Ennek támogatására továbbfejlesztettük a P-GRADE alkalmazások naplózhatóságát, és illesztettük a Condor feladatütemezőjéhez. Sikeresen kapcsoltunk a rendszerhez egy terhelés elosztó modult, mely képes a terheltségétől függően áthelyezni a folyamatokat. | We investigated novel approaches for performance analysis and optimization for efficient execution of grid applications, especially workflows. We took into consideration the special requirements of grid performance analysis when elaborated Mercury, a grid monitoring infrastructure. GRM, a performance monitor for parallel applications, has been integrated with R-GMA, a relational grid information system and Mercury as well. We developed Pulse and Prove visualisation tools for supporting grid performance analysis. We wrote a comprehensive state-of-the art survey of grid performance tools. We designed a novel abstraction layer of P-GRADE supporting workflows, and a grid portal. Users can draft and execute workflow applications in the grid via a web browser using the portal. The portal supports multiple grid implementations and provides monitoring capabilities for performance analysis. We tested the integration of the portal with grid resource brokers and also augmented it with some degree of fault-tolerance. Optimization may require the migration of parts of the application to different resources and thus, it requires support for checkpointing. We enhanced the checkpointing facilities of P-GRADE and coupled it to Condor job scheduler. We also extended the system with a load balancer module that is able to migrate processes as part of the optimization

Next-Generation EU DataGrid Data Management Services

We describe the architecture and initial implementation of the next-generation of Grid Data Management Middleware in the EU DataGrid (EDG) project. The new architecture stems out of our experience and the users requirements gathered during the two years of running our initial set of Grid Data Management Services. All of our new services are based on the Web Service technology paradigm, very much in line with the emerging Open Grid Services Architecture (OGSA). We have modularized our components and invested a great amount of effort towards a secure, extensible and robust service, starting from the design but also using a streamlined build and testing framework. Our service components are: Replica Location Service, Replica Metadata Service, Replica Optimization Service, Replica Subscription and high-level replica management. The service security infrastructure is fully GSI-enabled, hence compatible with the existing Globus Toolkit 2-based services; moreover, it allows for fine-grained authorization mechanisms that can be adjusted depending on the service semantics.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla,Ca, USA, March 2003 8 pages, LaTeX, the file contains all LaTeX sources - figures are in the directory "figures

Prediction of geometric errors of stamped sheet metal parts using deviation field decomposition

Stamping process is widely used to fabricate sheet-metal components. Due to the intrinsic nature of sheet-metal parts, it is usually difficult to control the quality of the final shape, surface defects or geometric errors. Additionally, to meet tight GD&T specifications, a proactive prediction technique is required to estimate global/local geometric defects caused by manufacturing steps. Current best practice relies on manual trial-and-error approaches which are far to be optimal and are costly and time consuming. This paper proposes a model-driven methodology to forecast geometric errors for given set of process parameters (forward process), and consequently optimise (feedback process) the process parameters to achieve given quality standards. The methodology is based on: (i) experimental investigation with varying process parameters and subsequently, deviation field extraction by mapping high density Cloud-of-Points with nominal CAD model; (ii) deviation field decomposition; (iii) surrogate model development by mapping decomposed deviation field to process parameters. An industrial case study is used to validate the methodology