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On the Performance of Oracle Grid Engine Queuing System for Computing Intensive Applications

  • Kolici, Vladi (Department of Electronic and Telecommunication, Polytechnic University of Tirana) ;
  • Herrero, Albert (Department of Computer Science, Universitat Politecnica de Catalunya) ;
  • Xhafa, Fatos (Department of Computer Science, Universitat Politecnica de Catalunya)
  • Received : 2014.10.14
  • Accepted : 2014.11.20
  • Published : 2014.12.31

Abstract

In this paper we present some research results on computing intensive applications using modern high performance architectures and from the perspective of high computational needs. Computing intensive applications are an important family of applications in distributed computing domain. They have been object of study using different distributed computing paradigms and infrastructures. Such applications distinguish for their demanding needs for CPU computing, independently of the amount of data associated with the problem instance. Among computing intensive applications, there are applications based on simulations, aiming to maximize system resources for processing large computations for simulation. In this research work, we consider an application that simulates scheduling and resource allocation in a Grid computing system using Genetic Algorithms. In such application, a rather large number of simulations is needed to extract meaningful statistical results about the behavior of the simulation results. We study the performance of Oracle Grid Engine for such application running in a Cluster of high computing capacities. Several scenarios were generated to measure the response time and queuing time under different workloads and number of nodes in the cluster.

References

  1. M. J. Fortin, G. M. Jacquez, and B. Shipley, "Computer intensive sampling methods in ecology," in Encyclopedia of Environmetrics. Chichester: Wiley, 2002, pp. 399-402.
  2. Q. Liu and G. Wainer, "Exploring multi-grained parallelism in compute-intensive DEVS simulations," in Proceedings of the 2010 IEEE Workshop on Principles of Advanced and Distributed Simulation, Atlanta, GA, 2010, pp. 1-8.
  3. S. Clemencon, A. Cousien, M. D. Felipe, and V. C. Tran, "On computer-intensive simulation and estimation methods for rare event analysis in epidemic models," Aug. 2013; http://arxiv.org/pdf/1308.5830v1.pdf.
  4. A. McKenna, M. Hanna, E. Banks, A. Sivachenko, K. Cibulskis, A. Kernytsky, ... and M. A. DePristo, "The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data," Genome Research, vol. 20, no. 9, pp. 1297-1303, 2010. https://doi.org/10.1101/gr.107524.110
  5. L. Yao, F. A. Rabhi, and M. Peat, "A case study in using ADAGE for compute-intensive financial analysis processes," in Proceedings of the 6th International Workshop on Enterprise Applications and Services in the Finance Industry, Barcelona, Spain, 2013, pp. 91-111.
  6. A. Niedzicka, "Computation-intensive image processing algorithm parallelization on multiple hardware architectures," in Proceedings of International Conference on Parallel Computing in Electrical Engineering (PARELEC2002), Warsaw, Poland, 2002, pp. 446-448.
  7. K. Goga, O. Terzo, P. Ruiu, and F. Xhafa, Simulation, modeling and performance evaluation tools for cloud applications," in Proceedings of the 8th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS2014), Birmingham, UK, 2014, pp. 226-232.
  8. V. Kolici, F. Xhafa, A. Herrero, and L. Barolli, "A study on the performance of Oracle Grid engine for computing intensive applications," in Proceedings of the 6th International Conference on Intelligent Networking and Collaborative Systems (INCoS2014), Salerno, Italy, 2014, pp. 282-288.
  9. H. Casanova, "Simgrid: a toolkit for the simulation of application scheduling," in Proceedings of the 1st IEEE/ACM International Symposium on Cluster Computing and the Grid, Brisbane, Australia, 2001, pp. 430-437.
  10. R. Buyya and M. Murshed, "Gridsim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing," Concurrency and Computation: Practice and Experience, vol. 14, no. 13-15, pp. 1175-1220, 2002. https://doi.org/10.1002/cpe.710
  11. F. Xhafa, J. Carretero, L. Barolli, and A. Durresi, "Requirements for an event-based simulation package for grid systems," Journal of Interconnection Networks, vol. 8, no. 2, pp. 163-178, 2007. https://doi.org/10.1142/S0219265907001965
  12. S. Phatanapherom, P. Uthayopas, and V. Kachitvichyanukul, "Dynamic scheduling II: fast simulation model for grid scheduling using HyperSim," in Proceedings of the 35th Conference on Winter Simulation: Driving Innovation (WSC2003), New Orleans, LO, 2003, pp. 1494-1500.
  13. F. Xhafa, L. Barolli, and D. Martos, "A web interface for the HyperSim-G Grid simulation package," in Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services (iiWAS2008), Linz, Austria, 2008, pp. 312-317.
  14. R. Buyya, R. Ranjan, and R. N. Calheiros, "Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: challenges and opportunities," in Proceedings of the International Conference on High Performance Computing & Simulation (HPCS'09), Leipzig, Germany, 2009, pp. 1-11.
  15. A. Nunez, J. L. Vazquez-Poletti, A. C. Caminero, G. G. Castane, J. Carretero, and I. M. Llorente, "iCanCloud: a flexible and scalable cloud infrastructure simulator," Journal of Grid Computing, vol. 10, no. 1, pp. 185-209, 2012. https://doi.org/10.1007/s10723-012-9208-5
  16. D. Kliazovich, P. Bouvry, and S. U. Khan, "GreenCloud: a packet-level simulator of energy-aware cloud computing data centers," in Proceedings of IEEE Global Telecommunications Conference (GLOBECOM 2010), Miami, FL, 2010, pp. 1-5.
  17. I. Sriram, "SPECI, a simulation tool exploring cloud-scale data centres," in Proceedings of the 1st International Conference on Cloud Computing, Beijing, China, 2009, pp. 381-392.
  18. J. Carretero, F. Xhafa, and A. Abraham, "Genetic algorithm based schedulers for grid computing systems," International Journal of Innovative Computing, Information and Control, vol. 3, no. 6, pp. 1-19, 2007.

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