Mobile Computing in Physics Analysis - An Indicator for eScience

This paper presents the design and implementation of a Grid-enabled physics analysis environment for handheld and other resource-limited computing devices as one example of the use of mobile devices in eScience. Handheld devices offer great potential because they provide ubiquitous access to data and round-the-clock connectivity over wireless links. Our solution aims to provide users of handheld devices the capability to launch heavy computational tasks on computational and data Grids, monitor the jobs status during execution, and retrieve results after job completion. Users carry their jobs on their handheld devices in the form of executables (and associated libraries). Users can transparently view the status of their jobs and get back their outputs without having to know where they are being executed. In this way, our system is able to act as a high-throughput computing environment where devices ranging from powerful desktop machines to small handhelds can employ the power of the Grid. The results shown in this paper are readily applicable to the wider eScience community.Comment: 8 pages, 7 figures. Presented at the 3rd Int Conf on Mobile Computing & Ubiquitous Networking (ICMU06. London October 200

Job Interactivity Using a Steering Service in an Interactive Grid Analysis Environment

Grid computing has been dominated by the execution of batch jobs. Interactive data analysis is a new domain in the area of grid job execution. The Grid-Enabled Analysis Environment (GAE) attempts to address this in HEP grids by the use of a Steering Service. This service will provide physicists with the continuous feedback of their jobs and will provide them with the ability to control and steer the execution of their submitted jobs. It will enable them to move their jobs to different grid nodes when desired. The Steering Service will also act autonomously to make steering decisions on behalf of the user, attempting to optimize the execution of the job. This service will also ensure the optimal consumption of the Grid user's resource quota. The Steering Service will provide a web service interface defined by standard WSDL. In this paper we have discussed how the Steering Service will facilitate interactive remote analysis of data generated in Interactive Grid Analysis Environment

Bulk Scheduling with the DIANA Scheduler

Results from the research and development of a Data Intensive and Network Aware (DIANA) scheduling engine, to be used primarily for data intensive sciences such as physics analysis, are described. In Grid analyses, tasks can involve thousands of computing, data handling, and network resources. The central problem in the scheduling of these resources is the coordinated management of computation and data at multiple locations and not just data replication or movement. However, this can prove to be a rather costly operation and efficient sing can be a challenge if compute and data resources are mapped without considering network costs. We have implemented an adaptive algorithm within the so-called DIANA Scheduler which takes into account data location and size, network performance and computation capability in order to enable efficient global scheduling. DIANA is a performance-aware and economy-guided Meta Scheduler. It iteratively allocates each job to the site that is most likely to produce the best performance as well as optimizing the global queue for any remaining jobs. Therefore it is equally suitable whether a single job is being submitted or bulk scheduling is being performed. Results indicate that considerable performance improvements can be gained by adopting the DIANA scheduling approach.Comment: 12 pages, 11 figures. To be published in the IEEE Transactions in Nuclear Science, IEEE Press. 200

The AliEn system, status and perspectives

AliEn is a production environment that implements several components of the Grid paradigm needed to simulate, reconstruct and analyse HEP data in a distributed way. The system is built around Open Source components, uses the Web Services model and standard network protocols to implement the computing platform that is currently being used to produce and analyse Monte Carlo data at over 30 sites on four continents. The aim of this paper is to present the current AliEn architecture and outline its future developments in the light of emerging standards.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 10 pages, Word, 10 figures. PSN MOAT00