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Secure-Sim-G: Security-Aware Grid Simulator - Basic Concept and Structure

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Języki publikacji
EN
Abstrakty
EN
Task scheduling and resource allocation are the key issues for computational grids. Distributed resources usually work at different autonomous domains with their own access and security policies that impact successful job executions across the domain boundaries. In this paper we present a security-aware grid simulator Secure-Sim-G, which facilitates the evaluation of the different scheduling heuristics under various scheduling criteria in several grid scenarios defined by the security conditions, grid size and system dynamics. The simulator allows the flexible activation or inactivation of all of the scheduling criteria and modules, which makes the application well adapted to the proper illustration of the different realistic scenarios and avoids the possible restriction to the specific scheduling resolution methods. The simulation results and traces may be graphically represented and stored at the server and can retrieved in different formats such as spreadsheets or pdf files.
Słowa kluczowe
Rocznik
Tom
Strony
33--42
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
autor
Bibliografia
  • [1] F. Xhafa, J. Carretero, L. Barolli, and A. Durresi, “Requirements for an event-based simulation package for grid systems”. J. Interconnection Netw., vol. 8, no 2, pp. 163–178, 2007.
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  • [3] K. Ranganathan and I. Foster, “Simulation studies of computation and data scheduling algorithms for data grids”, J. Grid Comput., vol. 1, no. 1, pp. 53–62, 2003.
  • [4] R. Buyya, and M. M. Murshed, “Grid-Sim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing”, Concurrency and Comput.: Practice and Experience, vol. 14, no. 13–15, pp. 1175–1220, 2002.
  • [5] O. Kang and S. Kang, “Web-based dynamic scheduling platform for grid computing”, Intern. J. Comp. Sci. Netwo. Secur., vol. 6, no. 5, pp. 67–75, 2006.
  • [6] S. Song, K. Hwang, and Y. K. Kwok, “Risk-resilient heuristics and genetic algorithms for security- assured grid job scheduling”, IEEE Trans. Comput., vol. 55, no. 6, pp. 703–719, 2006.
  • [7] S. Song, K. Hwang, and Y. K. Kwok, “Trusted grid computing with security binding and trust integration”, J. Grid Comput., vol. 3, no. 1-2, pp. 53–73, 2005.
  • [8] M. Humphrey and M. R. Thompson, “Security implications of typical grid computing usage scenarios”, in Proc. 10th IEEE Int. Symp. High Performa. Distrib. Comput., San Francisco, CA , USA, 2001.
  • [9] E. Cody, R. Sharman, R. H. Rao, and S. Upadhyaya, “Security in grid computing: a review and synthesis”, Decision Supp. Sys., vol. 44, pp. 749–764, 2008.
  • [10] S. Hwang and C. Kesselman, “A flexible framework for fault tolerance in the grid”, J. Grid Comput., vol. 1, no. 3, pp. 251–272, 2003.
  • [11] J. Abawajy, “An efficient adaptive scheduling policy for high performance computing”, Future Gener. Comp. Sys., vol. 25, no. 3, pp. 364–370, 2009.
  • [12] J. Kołodziej and F. Xhafa, “Meeting security and user behaviour requirements in grid scheduling”, Simul. Modell. Practice and Theory, vol. 19, no. 1, pp. 213–226, 2011.
  • [13] J. Kołodziej, F. Xhafa, and M. Bogdański, “Secure and task abortion aware ga-based hybrid metaheuristics for grid scheduling”, in PPSN XI, Schaefer et al., Eds. LNCS, vol. 6238, 2010, pp. 526–535.
  • [14] J. Kołodziej and F. Xhafa, “Integration of Task Abortion and Security Requirements in GA-based Meta-Heuristics for Independent Batch Grid Scheduling”, Computers and Mathematics with Applications, DOI: 10.1016/j.camwa.2011.07.038, 2011.
  • [15] J. Kołodziej and F. Xhafa, “A game-theoretic and hybrid genetic meta-heuristic model for security-assured scheduling of independent jobs in computational grids”, in Proc. CISIS 2010, USA: IEEE Press, 2010, pp. 93–100.
  • [16] S. Ali, H. J. Siegel, M. Maheswaran, and D. Hensgen, “Task execution time modeling for heterogeneous computing systems”, in Proc. 9th Heterogen. Comput. Worksh. HCW 2000, Cancun, Mexico, 2000, pp. 185–199.
  • [17] F. Xhafa, J. Carretero, E. Alba, and B. Dorronsoro, “Tabu search algorithm for scheduling independent jobs in computational grids”, Comp. Informatics J., special issue on Intelligent Computational Methods, J. Burguillo-Rial, J. Kołodziej, and L. Nolle, Eds., vol. 28, no. 2, pp 237–249, 2009.
  • [18] F. Xhafa and A. Abraham, “Meta-heuristics for grid scheduling problems”, in Meta-heuristics for Scheduling in Distributed Computing Environments, Chapter 1, Series Studies in Computational Intelligence, Springer, 2009, pp. 1–37.
  • [19] F. Xhafa, J. Carretero, and A. Abraham, “Genetic algorithm based schedulers for grid computing systems”, Int. J. Innovative Comput. Informa. Control, vol. 5, pp. 1–19, 2007.
  • [20] F. Pinel, J. E. Pecero, P. Bouvry, and S. U. Khan, “A two-phase heuristic for the scheduling of independent tasks on computational grids”, in Proc. ACM/IEEE/IFIP Int. Conf. High Performa. Comput. Simulation HPCS 2011, Istanbul, Turkey, 2011.
  • [21] J. Kołodziej, F. Xhafa, and Ł. Kolanko, “Hierarchic genetic scheduler of independent jobs in computational grid environment”, in Proc. 23rd Euro. Conf. Modell. Simulation ECMS 2009, Madrid, Spain, 2009, J. Otamendi, A. Bargieła, J. L. Montes and L. M. Doncel Pedrera, Eds. Dudweiler, Germany: IEEE Press, 2009, pp. 108–115.
  • [22] J. Kołodziej and F. Xhafa, “Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population”, Future Generation Computer Systems, vol. 27 (2011), pp. 1035–1046, DOI: 10.1016/j.future.2011.04.011, 2011.
  • [23] F. Xhafa, J. Carretero, and A. Abraham, “Genetic algorithm based schedulers for grid computing systems”, Int. J. Innovative Comput. Informa. Control, vol. 3, no. 5, pp. 1053–1071, 2007.
  • [24] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. Springer, 1992.
  • [25] J. Kołodziej, S. U. Khan, and F. Xhafa, “Genetic algorithms for energy-aware scheduling in computational grids”, in Proc. 6th IEEE Int. Conf. P2P, Parallel, Grid, Cloud, Internet Comput. 3PGCIC, Barcelona, Spain, 2011.
  • [26] D. Kliazovich, P. Bouvry, Y. Audzevich, and S. U. Khan, “Green- Cloud: a packet-level simulator of energy-aware cloud computing data centers”, in Proc. 53rd IEEE Global Commun. Conf. Globecom 2010, Miami, FL, USA, 2010.
  • [27] S. U. Khan and I. Ahmad, “A cooperative game theoretical technique for joint optimization of energy consumption and response time in computational grids”, IEEE Trans. Parallel and Distrib. Sys., vol. 20, no. 3, pp. 346–360, 2009.
  • [28] S. U. Khan, “A goal programming approach for the joint optimization of energy consumption and response time in computational grids”, in Proc. 28th IEEE Int. Performa. Comput. Commun. Conf. IPCCC 2009, Phoenix, AZ, USA, 2009, pp. 410–417.
  • [29] S. U. Khan and I. Ahmad, “Non-cooperative, semi-cooperative, and cooperative games-based grid resource allocation”, in Proc. 20th IEEE Int. Parallel Distrib. Process. Symp. IPDPS 2006, Rhodes Island, Greece, 2006.
  • [30] G. L. Valentini et al., “An overview of energy efficiency techniques in cluster computing systems”, Cluster Computing, DOI: 10.1007/s10586-011-0171-x, 2011.
  • [31] L. Wang and S. U. Khan, “Review of performance metrics for green data centers: a taxonomy study”, J. Supercomput., pp. 1–18. DOI:10.1007/s11227-011-0704-3, 2011.
  • [32] S. Zeadally, S. U. Khan, and N. Chilamkurti, “Energy-efficient networking: past, present, and future”, J. Supercomput., pp. 1–26. DOI:10.1007/s11227-011-0632-2, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BATA-0015-0004
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