Abstract
This paper investigates an alternative way of efficiently matching and allocating grid resources to user jobs, in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of Genetic Algorithm, using populations based on Multisets is presented. For the proposed resource allocation method, an additional mechanism (populations based on multiset) is introduced into the genetic algorithm components, to enhance its search capability in a large problem space. A computational experiment is presented in order to show the importance of operator improvement on traditional genetic algorithms. The preliminary performance results show that the introduction of an additional operator fine-tuning is efficient in both speed and precession, and can keep up with the high job arrival rates.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Manso, A., Correia, L.: Genetic algorithms using populations based on multisets. In: New Trends in Artificial Intelligence, EPIA, pp. 53–64 (2009)
Singh, D., Ibrahim, A., Yohanna, T., Singh, J.: An overview of the applications of multisets. Novi Sad J. Math. 37(3), 73–92 (2007)
Davis, L.: Job shop scheduling with genetic algorithms. In: Proceedings of an International Conference on Genetic Algorithms and their Applications. Lawrence Erlbaum Associates, Pittsburgh (1985)
Davis, E.W., Heidorn, G.E.: An algorithm for optimal project scheduling under multiple resource constraints. Manage. Sci. 17.12 (1971): B-803–b817
Davis, E.W., Patterson, J.H.: A comparison of heuristic and optimum solutions in resource-constrained project scheduling. Manage. Sci. 21(8), 944–955 (1975)
Abdulal, W., Ramachandram, S.: Reliability-aware genetic scheduling algorithm in grid environment. In: IEEE International Conference on Communication Systems and Network Technologies, pp. 673–677. IEEE, Katra, Jammu, India, June (2011). ISBN: 978-0-7695-4437-3/11
Abdulal, W., Jadaan, O. A., Jabas, A., Ramachandram, S.: Mutation based simulated annealing algorithm for minimizing makespan in grid computing systems. In: IEEE International Conference on Network and Computer Science (ICNCS 2011), vol. 6, pp. (90–94). IEEE, Kanyakumari, India, April (2011). ISBN: 978-1-4244-8679-3
Carretero, J. Xhafa, F.: Using genetic algorithms for scheduling jobs in large scale grid applications. J. Technol. Econ. Dev. 12, 11–17 (2006). http://citeseer.ist.psu.edu, ISSN: 1392-8619 print/ISSN: 1822-3613 Online
Carretero, J., Xhafa, F., Abraham, A.: Genetic algorithm based schedulers for grid computing systems. Int. J. Innovative Comput. Inf. Control 3(6), 1–19 (2007)
Zhang, L., Chen, Y., Sun, R., Jing, S., Yang, B.: A task scheduling algorithm based on PSO for grid computing. Int. J. Comput. Intell. Res. 4(1), 37–43 (2008)
Di Martino, V., Mililotti, M.: Sub optimal scheduling in a grid using genetic algorithms. Parallel Comput. 30, 553–565 (2004)
Priya, S.B., Prakash, M., Dhawan, K.K.: Fault tolerance-genetic algorithm for grid task scheduling using check point. In: Sixth International Conference on Grid and Cooperative Computing, GCC 2007, pp. 676–680. IEEE (2007)
Gao, Y., Rong, H., Huang, J.Z.: Adaptive grid job scheduling with genetic algorithms. Future Gener. Comput. Syst. 21(1), 151–161 (2005)
Abraham, A., Buyya, R., Nath, B.: Nature’s heuristics for scheduling jobs on computational grids. In: The 8th IEEE International Conference on Advanced Computing and Communications (ADCOM 2000), India (2000)
Wieder, T.: Generation of all possible multiselections from a multiset. Prog. Appl. Math. 2(1), 61–66 (2011)
Aparício, J.N., Correia, L., Moura-Pires, F.: Expressing population based optimization heuristics using PLATO. EPIA 1999, 367–383 (1999)
Ibrahim, A.M., Ezugwu, A.E.S., Abdulsalami, A.: Computational model for cardinality bounded multiset space. Int. J. Appl. Math. Res. 1(3), 330–341 (2012)
Blizard, W.: Multiset theory. Notre Dame J. Formal Logic 30(1), 36–66 (1989)
Golberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addion wesley, Boston (1989)
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs. Springer Science & Business Media, New York (2013)
Campegiani, P.: A genetic algorithm to solve the virtual machines resources allocation problem in multi-tier distributed systems. In: Second International Workshop on Virtualization Performance: Analysis, Characterization, and Tools (VPACT 2009), Boston, Massachusett (2009)
Hugh, M.C.: Getting the timing right—the use of genetic algorithms in scheduling. In: Proceeding of Adaptive computing and information processing conference (UNISYS 1994), pp 393–411. Brunel, London (1994)
Acknowledgments
We would like to thank the corps member, Nneoma Okoroafor and Bridget Pwajok, who worked with us during the initial preparation of this paper. We also thank all the anonymous reviewers for their comments and recommendations, which have been crucial to improving the quality of this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Ezugwu, A.E., Yakmut, D.I., Ochang, P.A., Buhari, S.M., Frincu, M.E., Junaidu, S.B. (2016). Multiset Genetic Algorithm Approach to Grid Resource Allocation. In: Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-28031-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-28031-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28030-1
Online ISBN: 978-3-319-28031-8
eBook Packages: EngineeringEngineering (R0)