Abstract
Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data storage or network resources across dynamic and geographically dispersed organizations. The goal of grid task scheduling is to achieve high system throughput and to match the application needed with the available computing resources. This is matching of resources in a non-deterministically shared heterogeneous environment. The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. To obtain good methods to solve this problem a new area of research is implemented. This area is based on developed heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper we introduce a tasks scheduling algorithm for grid computing. The algorithm is based on Ant Colony Optimization (ACO) which is a Monte Carlo method. The paper shows how to search for the best tasks scheduling for grid computing.
This is a preview of subscription content, log in via an institution to check access.
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
Braun, T.D., Siegel, H.J., Beck, N., Bolony, L., Maheswaram, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Jao, B.: A taxonomy for describing matching and scheduling heuristics for mixed-machine heterogeneous computing systems. In: IEEE Workshop on Advances in Parallel and Distributed Systems, pp. 330–335 (1998)
Dorigo, M., Di Caro, G.: The Ant Colony Optimization metaheuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Idea in Optimization, pp. 1–32. McGraw-Hill, New York (1999)
Dorigo, M., Gambardella, L.M.: Ant Colony System: A cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transaction on Evolutionary Computation 1, 53–66 (1999)
Fernandez-Baca, D.: Allocating Modules to Processors in a Distributed System. IEEE Transactions on Software Engineering 15(11), 1427–1436 (1989)
Freund, R.F., Gherrity, M., Ambrosius, S., Camp-Bell, M., Halderman, M., Hensgen, D., Keith, E., Kidd, T., Kussow, M., Lima, J.D., Mirabile, F., Moore, L., Rust, B., Siegel, H.J.: Scheduling Resources in Multi-User Heterogeneous Computing Environments with SmartNet. In: IEEE Heterogeneous Computing Workshop, pp. 184–199 (1998)
Gong, L., Sun, X.H., Waston, E.: Performance Modeling and Prediction of Non- Dedicated Network Computing. IEEE Transaction on Computer 51(9), 1041–1055 (2002)
Maheswaran, M., Ali, S., Siegel, H.J., Hensgen, D., Freund, R.: Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems. In: 8th IEEE Heterogeneous Computing Workshop (HCW 1999), San Juan, Puerto Rico, pp. 30–44 (1999)
Pinedo, M.: Scheduling: Theory, Algorithms and Systems. Prentice Hall, Englewood Clifts (1995)
Rubinstein, R.Y.: Simulation and the Monte Carlo Method. John Wiley & Sons, Chichester (1981)
Schopf, J.M.: A General Architecture for Scheduling on the Grid. Special issue of JPDC on Grid Computing (2002)
Sih, G.C., Lee, E.A.: A Compile-Time Scheduling Heuristic for Inter Connection- Constrained Heterogeneous Processor Architectures. IEEE Transactions Parallel and Distributed Systems 4, 175–187 (1993)
Strelsov, S., Vakili, P.: Variance Reduction Algorithms for Parallel Replicated Simulation of Uniformized Markov Chains. J. of Discrete Event Dynamic Systems: Theory and Applications 6, 159–180 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fidanova, S., Durchova, M. (2006). Ant Algorithm for Grid Scheduling Problem. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2005. Lecture Notes in Computer Science, vol 3743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11666806_46
Download citation
DOI: https://doi.org/10.1007/11666806_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31994-8
Online ISBN: 978-3-540-31995-5
eBook Packages: Computer ScienceComputer Science (R0)