DAGITIZER – A Tool to Generate Directed Acyclic Graph through Randomizer to Model Scheduling in Grid Computing

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (volume 167)

Abstract

Scheduling is absolutely the resource management. A group of interdependent jobs/tasks forms the workflow application and scheduling is to map the jobs/tasks on to the collection of heterogeneous resources available in a massive geographic spread. Most complicated applications consist of interdependent jobs that coordinate to solve a problem. The completion of a particular job is the criterion function essentially to meet in order to start the execution of those jobs that depend upon it [1]. This kind of workflow application may be represented in the form of a Directed Acyclic Graph (DAG). Grid Workflow is such an application and is modeled by DAG. This paper proposes a tool that generates Directed Acyclic Graph through Randomizer, which helps in solving the scheduling problem among the dependent tasks by considering the parameters, computation cost (COMPCost) of the nodes and the communication cost (COMMCost) between the nodes. This tool is developed in Java, considering it as a platform independent and web authoring application developer. The task dependencies are made random, the computation cost and communication cost are also randomly allocated by the randomizer. The output generated by the tool includes (i) a visual component of an actual DAG,(ii) a table with complete information on task, its predecessors, COMPCost, COMMCost and (iii) detailed description about the number of levels, number of tasks at each level, identification of a tasks in a level and relationship between the nodes.

Keywords

Grid Workflow Scheduling Directed Acyclic Graph Randomizer Communication cost Computation cost 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lopez, M.M., Heymann, E., Senar, M.A.: Analysis of Dynamic Heuristics for Workflow Scheduling on Grid Systems. In: IEEE Proceedings of The Fifth International Symposium on Parallel and Distributed Computing (2006)Google Scholar
  2. 2.
    Christofides, N.: Graph theory: an algorithmic approach, pp. 170–174. Academic Press (1975)Google Scholar
  3. 3.
    Thulasiraman, K., Swamy, M.N.S.: Acyclic Directed Graphs. In: Graphs: Theory and Algorithms. John Wiley and Son (1992) ISBN 9780471513568Google Scholar
  4. 4.
    Bang-Jensen, J.: 2.1 Acyclic Digraphs, Digraphs: Theory, Algorithms and Applications, 2nd edn. Springer Monographs in Mathematics, pp. 32–34. Springer (2008)Google Scholar
  5. 5.
    Hwang, K.: Advanced Computer Architecture: Parallelism, Scalability, Programmability. McGraw-Hill, Inc., New York (1993)Google Scholar
  6. 6.
    Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-Effective and Low Complexity Task Scheduling for Heterogeneous Computing. IEEE Trans. Parallel Distributed Systems 13(3), 260–274 (2005)CrossRefGoogle Scholar
  7. 7.
    Ilavarasan, E., Thambidurai, P., Mahilmannan, R.: Performance Effective Task Scheduling Algorithm For Heterogeneous Computing System. In: Proceedings of the Fourth International Symposium on Parallel and Distributed Computing, France, pp. 28–38 (2005)Google Scholar
  8. 8.
    Sih, G.C., Lee, E.A.: A Compile-Time Scheduling Heuristic For Interconnection-Constrained Heterogeneous Processor Architectures. IEEE Trans. Parallel Distributed Systems 4(2), 175–187 (1993)CrossRefGoogle Scholar
  9. 9.
    Kim, J., Rho, J., Lee, J.-O., Ko, M.-C.: CPOC: “Effective Static Task Scheduling For Grid Computing”. In: Proceedings of the 2005 International Conference on High Performance Computing and Communications, Italy, pp. 477–486 (2005)Google Scholar
  10. 10.
    Kwok, Y.-K., Ahmad, I.: Static Algorithms for Allocating Directed Task Graphs to Mutiporcessors. ACM Computing Surveys 31(4) (December 1999)Google Scholar
  11. 11.
    Yang, T., Gerasoulis, A.: PYROSS: Static Task Scheduling and Code Generation for Message Passing Multiprocessors. In: Kennedy, K., Polychronopoulos, C.D. (eds.) Proceedings of 1992 International Conference on Super Computing (ICS 1992), Washington DC, July 19-23, pp. 428–437. ACM press, New York (1992)Google Scholar
  12. 12.
    Shirazi, B., Kavi, K., Hurson, A.R., Biswas, P.: PARSA: A Parallel Program Scheduling and Assessment Environment. In: Proceedings of the International Conference on Parallel Processing, pp. 68–72. CRC Press Inc., Boca Raton (1993)Google Scholar
  13. 13.
    Chu, W.W., Lan, M.T., Hellerstein, J.: Estimation of Intermodule Communication (IMC) and its Applications in Distributed Processing Systems. IEEE Transactions and Computing C-33, 691–699 (1984)CrossRefGoogle Scholar
  14. 14.
    Hu, T.C.: Parallel Sequencing and Assembly Line Problems. Operational Research 19, 841–848 (1961)Google Scholar
  15. 15.
    Schildt, H.: The Complete Reference Java2, 5th edn. Tata McGraw Hill Publishing Company (2002)Google Scholar
  16. 16.
    Papadimitriou, C.H.: Computational complexity. Addison-Wesley, Reading (1994) ISBN 0-201-53082-1MATHGoogle Scholar
  17. 17.
    Sipser, M.: Introduction to the Theory of Computation. Course Technology Inc. (2006) ISBN 0-619-21764-2Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceJamal Mohamed CollegeTrichirappalliIndia
  2. 2.Department of Computer ScienceSRM UniversityChennaiIndia

Personalised recommendations