Abstract
The utilization of the computational Grid processor network has become a common method for researchers and scientists without access to local processor clusters to avail of the benefits of parallel processing for compute-intensive applications. As a result, this demand requires effective and efficient dynamic allocation of available resources. Although static scheduling and allocation techniques have proved effective, the dynamic nature of the Grid requires innovative techniques for reacting to change and maintaining stability for users. The dynamic scheduling process requires quite powerful optimization techniques, which can themselves lack the performance required in reaction time for achieving an effective schedule solution. Often there is a trade-off between solution quality and speed in achieving a solution. This paper presents an extension of a technique used in optimization and scheduling which can provide the means of achieving this balance and improves on similar approaches currently published.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fernandez-Baca, D.: Allocating Modules to Processors in a Distributed System. IEEE Transactions on Software Engineering 15(11), 1427–1436 (1989)
Tracy, D., et al.: A Comparison of eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. Journal of Parallel and Distributed Computing 61, 810–837 (2001)
Liu, L., Zhan, J., Lian, L.: A Runtime Scheduling Approach with Respect to Job Parallelism for Computational Grid. In: Proc. Of 3rd International Conference of Grid and Cooperative Computing (2004)
Mika, M., et al.: A Metaheuristic Approach to Scheduling Workflow Jobs on a Grid. In: Grid Resource Management: State of the Art and Future Trends, Kluwer Academic Publishers, Boston (2003)
Dueck, G.: Threshold Accepting: A General Purpose Optimization Algorithm Appearing Superior to Simulated Annealing. J. Computational Physics 90, 161–175 (1990)
Kirkpatrick, S., Gellat, J.C.D., Vecci, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)
Yarkhan, A., Dongarra, J.: Experiments with Scheduling Using Simulated Annealing in a Grid Environment. In: Parashar, M. (ed.) GRID 2002. LNCS, vol. 2536, pp. 232–242. Springer, Heidelberg (2002)
Fidanova, S.: Simulated Annealing for Grid Scheduling Problem. In: IEEE John Vincent Atanasoft International Symposium on Modern Computing (JVA 2006), pp. 41–45 (2006)
McMullan, P.: An Extended Implementation of the Great Deluge Algorithm for Course Timetabling. In: ICCS 2007. International Conference on Computational Science. LNCS, Springer, Heidelberg (2007)
Kendall, G., Mohamad, M.: Channel Assignment in Cellular Communication Using a Great Deluge Hyper-Heuristic. In: Proc. of IEEE International Conference on Network (ICON 2004), pp. 769–773 (2004)
Petrovic, S., Burke, E.K.: University Timetabling, Handbook of Scheduling: Algorithms, Models and Performance Analysis, ch. 45. CRC Press, Boca Raton (2004)
McMullan, P., Roche, T.: An Intelligent Space Allocation and Planning Tool for Educational Requirements. Technical Report, RTS-TR-2005-2 (2005)
Berman, F., et al.: The GrADS Project: Software Support for high-level Grid application development. Int. Journal of High Performance Computing Applications 15(4), 327–344 (2001)
Foster, I., Kesselman, C.: The Globus Toolkit. In: Foster, I., Kesselmanm, C. (eds.) The Grid: Blueprint for a New Computing Infrastructure, ch. 11, Morgan Kaufmann, San Francisco (1999)
Wolski, R., Spring, N., Hayes, J.: The Network Weather Service: a Distributed Resource Performance Forecasting System for Metacomputing. Future Generation Computing Systems 15(5-6), 757–768 (1999)
Burke, E.K., Newall, J.P.: Solving Examination Timetabling Problems through Adaptation of Heuristic Orderings, Technical Report, Nottingham (2002)
Abramson, D., Krishnamoorthy, M., Dang, H.: Simulated Annealing Cooling Schedules for the School Timetabling Problem. Asia-Pacific Journal of Operation Research 16, 1–22 (1999)
Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Journal Structural and Multidisciplinary Optimization 26(6), 369–395 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
McMullan, P., McCollum, B. (2007). Dynamic Job Scheduling on the Grid Environment Using the Great Deluge Algorithm. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2007. Lecture Notes in Computer Science, vol 4671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73940-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-73940-1_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73939-5
Online ISBN: 978-3-540-73940-1
eBook Packages: Computer ScienceComputer Science (R0)