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A Comparative Analysis of Adaptive Solutions for Grid Environments

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Abstract

Grid computing environments are distributed systems composed by heterogeneous and geographically distributed resources. This type of systems mainly emerged to satisfy the increasing computing power demand within the scientific community. Despite the advantages of such paradigm, there are still several challenges related to the discovery, monitoring and selection of grid resources. Moreover, the dynamic nature and changing characteristics of such environments worsen the applications performance. Thus, improving their efficiency is a fundamental issue. The present contribution analyses two self-adaptive solutions focused on enhancing the grid resource selection process by using resources in an efficient way. On the one hand, the Efficient Resources Selection model which is defined from the user’s point of view (it avoids controlling or modifying the infrastructure) and it is based on the Scatter Search method for achieving a suitable selection of resources. On the other hand, Montera2, a framework designed for addressing an efficient execution of distributed applications on the grid; it defines and employs a dynamic scheduling algorithm to determine the size and number of tasks to be executed. Both approaches have been tested on a real European infrastructure belonging to the well-known European Grid Infrastructure (EGI) project. The study also compares both solutions with the standard scheduling technique that governs this infrastructure, the gLite WMS scheduler, showing a much better performance by reducing the final makespan by a factor of 20 if compared to the gLite WMS scheduler. An analysis of task and time overheads for both approaches is also included. Furthermore, comparisons with many other solutions proposed in the literature are presented, showing the advantages of our approaches.

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Notes

  1. The Computing Element, CE, is a set of services located in every grid site that provides access for Grid jobs to a local resource management system.

  2. When the performance of the infrastructure gets worse there are very few actions that users can carry out to mitigate it.

  3. http://www.egi.eu/.

  4. http://www.globus.org/.

  5. The Information System, IS, records information about both the status of resources and tasks.

  6. http://fusion.bifi.unizar.es/?page_id=118.

  7. http://www.egi.eu.

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Acknowledgments

Antonio Juan Rubio-Montero, from CIEMAT, was a great support on the creation and debugging of Montera2 and the administration of the local resources employed. Results obtained in this paper were computed on the European Grid Infrastructure (http://www.egi.eu) and its European Commission co-funded project EGI-InSPIRE (RI-261323). The authors thank the European Grid Infrastructure and supporting National Grid Initiatives for providing the technical support, computing and storage facilities. Authors have also count on the support from COST Action BETTY (IC1201). Last but not least, the authors would also like to acknowledge the support of the European Funds for Regional Development.

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Authors and Affiliations

  1. CETA-CIEMAT, C. Sola 1, 10200, Trujillo, Spain

    María Botón-Fernández & Francisco Prieto-Castrillo

  2. CIEMAT, Avda Complutense 22, 28040, Madrid, Spain

    Manuel Rodríguez-Pascual & Rafael Mayo-García

  3. Escuela Politécnica, University of Extremadura, Avda Universidad s/n, 10003, Cáceres, Spain

    Miguel A. Vega-Rodríguez

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  1. María Botón-Fernández
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Correspondence to Manuel Rodríguez-Pascual.

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Botón-Fernández, M., Rodríguez-Pascual, M., Vega-Rodríguez, M.A. et al. A Comparative Analysis of Adaptive Solutions for Grid Environments. Int J Parallel Prog 43, 786–811 (2015). https://doi.org/10.1007/s10766-014-0342-5

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