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Tytuł artykułu

Identifying limits of scalability in distributed, heterogeneous, layer based monitoring concepts like SLAte

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper we present the concept of a scalable job centric monitoring infrastructure. The overall performance of this distributed, layer based architecture called SLAte can be increased by installing additional servers to adapt to the demands of the monitored resources and users. Another important aspect is to offer a uniform global view on all data which are stored distributed to provide an easy access for users or visualisation tools. Additionally we discus the impact of these uniform access layer on scalability.
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Czasopismo
Rocznik
Strony
23--33
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
  • Center for Information Services and High Performance Computing (ZIH), Technische Universitat Dresden
  • Center for Information Services and High Performance Computing (ZIH), Technische Universitat Dresden
Bibliografia
  • [1] A home for public OpenPBS patches, tools, and information. http://www.mcs.anl.gov/research/projects/openpbs/
  • [2] GNOME Documentation Library – System Monitor Manual V2.2. http://library.gnome.org/users/gnome-system-monitor/stable/index.html.en
  • [3] GNU gprof. http://sourceware.org/binutils/docs/gprof/
  • [4] pound. http://www.apsis.ch/pound/
  • [5] Procps – The /proc file system utilities. http://procps.sourceforge.net/index.html
  • [6] TORQUE Resource Manager. http://www.clusterresources.com/products/torque-resource-manager.php
  • [7] Andreeva J., Calloni M., Colling D., Fanzago F., D’Hondt J., Klem J., Maier G., Letts J., Maes J., Padhi S., Sarkar S., Spiga D., Mulders P. V., Villella I.: CMS Analysis Operations. Journal of Physics: Conference Series, 219(7):072007, 2010.
  • [8] Baur T., Breu R., Kalman T., Lindinger T., Milbert A., Poghosyan G., Reiser H., Romberg M.: An Interoperable Grid Information System for Integrated Resource Monitoring Based on Virtual Organizations. Journal of Grid Computing, 7:319–333, 2009.
  • [9] Brunst H., Hackenberg D., Juckeland G., Rohling H.: Comprehensive Performance Tracking with Vampir 7. In Muller M. S., Resch M. M., Schulz A., Nagel W. E., editors, Tools for High Performance Computing 2009, pp. 17–29. Springer Berlin Heidelberg, 2010.
  • [10] Brunst H., Malony A., Shende S., Bell R.: Online Remote Trace Analysis of Parallel Applications on High-Performance Clusters. In Veidenbaum A., Joe K., Amano H., Aiso H., editors, High Performance Computing, volume 2858 of Lecture Notes in Computer Science, pp. 440–449. Springer Berlin / Heidelberg, 2003.
  • [11] Elmroth E., Gardfj¨all P., Mulmo O., Ake Sandgren., Sandholm T.: A Coordinated Accounting Solution for SweGrid Version: Draft 0.1.3 Date: October 7, 2003.
  • [12] Foster I.: Globus Toolkit Version 4: Software for Service-Oriented Systems. In Jin H., Reed D., Jiang W., editors, Network and Parallel Computing, volume 3779 of Lecture Notes in Computer Science, pp. 2–13. Springer Berlin / Heidelberg, 2005.
  • [13] Hilbrich M., Muller-Pfefferkorn R.: A Scalable Infrastructure for Job-Centric Monitoring Data from Distributed Systems. In Bubak M., Turala M., Wiatr K., editors, Proc. Cracow Grid Workshop ’09, pp. 120–125, ul. Nawojki 11, 30-950 Krakow 61, P.O. Box 386, Poland, February 2010. ACC CYFRONET AGH.
  • [14] Lorenz D., Borovac S., Buchholz P., Eichenhardt H., Harenberg T., M¨attig P., Mechtel M., M¨uller-Pfefferkorn R., Neumann R., Reeves K., Uebing C., Walkowiak W., William T., Wism¨uller R.: Job monitoring and steering in D-Grid’s 32 Marcus Hilbrich, Ralph M¨uller-Pfefferkorn High Energy Physics Community Grid. Future Gener. Comput. Syst., 25:308–314, March 2009.
  • [15] Muller-Pfefferkorn R., Neumann R., Hammad A., Harenberg T., Husken M., Mattig P., Mechtel M., Meder-Marouelli D., Borovac S., Ueberholz P.: Monitoring of Jobs and their Execution for the LHC Computing Grid. 2006.
  • [16] Muller-Pfefferkorn R., Neumann R., William T.: AMon a User-Friendly Job Monitoring for the Grid. In Priol T., Vanneschi M., editors, CoreGRID, pp. 185–192. Springer, 2007.
  • [17] Piro Rosario M., Andrea G., Giuseppe P., Albert W.: Using historical accounting information to predict the resource usage of grid jobs. Future Generation Computer Systems, 25(5):499–510, 2009.
  • [18] Shende S. S., Malony A. D.: The Tau Parallel Performance System. International Journal of High Performance Computing Applications, 20(2):287–311, Summer 2006.
  • [19] Sottile M. J., Minnich R. G.: Supermon: A High-Speed Cluster Monitoring System. In Proc. of IEEE Intl. Conference on Cluster Computing, pp. 39–46, 2002.
  • [20] Vanderster D. C., Brochu F., Cowan G., Egede U., Elmsheuser J., Gaidoz B., Harrison K., Lee H. C., Liko D., Maier A., Mo´scicki J. T., Muraru A., Pajchel K., Reece W., Samset B., Slater M., Soroko A., Tan C. L., Williams M.: Ganga: User-friendly Grid job submission and management tool for LHC and beyond. Journal of Physics: Conference Series, 219(7):072022, 2010.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-AGH1-0031-0002
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