Abstract
In principle, distributed heterogeneous commodity clusters can be deployed as a computing platform for parallel execution of user application, however, in practice, the tasks of first discovering and then configuring resources to meet application requirements are difficult problems. This paper presents a general-purpose resource selection framework that addresses the problems of resources discovery and configuration by defining a resource selection scheme for locating distributed resources that match application requirements. The proposed resource selection method is based on the frequencies of weighted condition attribute values of resources and the outstanding overall searching ability of genetic algorithm. The concept of soft set condition attributes reducts, which is dependent on the weighted conditions’ attribute value of resource parameters is used to achieve the required goals. Empirical results are reported to demonstrate the potential of soft set condition attribute reducts in the implementation of resource selection decision models with relatively higher level of accuracy.
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A.E. Ezugwu, M.E. Frincu, S.B. Junaidu, Performance characterization of heterogeneous distributed commodity cluster resources, in 2014 IEEE 6th International Conference on Adaptive Science and Technology (ICAST) (IEEE, 2014), pp. 1–8
G.F. Coulouris, J. Dollimore, T. Kindberg, Distributed Systems: Concepts and Design (Pearson Education, 2005)
R. Buyya, D. Abramson, J. Giddy, Nimrod/G: An architecture for a resource management and scheduling system in a global computational grid, in The Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region, 2000. Proceedings, vol. 1 (IEEE, 2000), pp. 283–289
D. Molodtsov, Soft set theory—first results. Comput. Math Appl. 37(4), 19–31 (1999)
P.K. Maji, R. Biswas, A. Roy, Soft set theory. Comput. Math Appl. 45(4), 555–562 (2003)
D.A. Kumar, R. Rengasamy, Parameterization reduction using soft set theory for better decision making, in 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME) (IEEE, 2013), pp. 365–367
D.K. Sut, An application of fuzzy soft relation in decision making problems. Int. J. Math. Trends Technol. 3(2), 51–54 (2012)
K. Gong, Z. Xiao, X. Zhang, The bijective soft set with its operations. Comput. Math Appl. 60(8), 2270–2278 (2010)
N. Çağman, I. Deli, Products of FP-soft sets and their applications. Hacettepe J. Math. Stat. 41(3) (2012)
N. Çağman, F. Çıtak, S. Enginoğlu, Fuzzy parameterized fuzzy soft set theory and its applications. Turk. J. Fuzzy Syst. 1(1), 21–35 (2010)
S. Alkhazaleh, A.R. Salleh, N. Hassan, Soft multisets theory. Appl. Math. Sci. 5(72), 3561–3573 (2011)
Y. Zhao, F. Luo, S.K.M. Wong, Y.Y. Yao, A general definition of an attribute reduct. Lect. Notes Artif. Intell. 4481, 101–108 (2007)
P.K. Maji, A.R. Roy, R. Biswas, An application of soft sets in a decision making problem. Comput. Math. Appl. 44(8), 1077–1083 (2002)
K. Sastry, D.E. Goldberg, G. Kendall, Genetic algorithms. In Search methodologies. (Springer, US, 2014), pp. 93–117
K.F. Man, K.S. Tang, S. Kwong, Genetic algorithms: concepts and applications. IEEE Trans. Ind. Electron. 43(5), 519–534 (1996)
A. Ezugwu, N. Okoroafor, S. Buhari et al., Grid resource allocation with genetic algorithm using population based on multisets. J. Intell. Syst. (2016). https://doi.org/10.1515/jisys-2015-0089. Accessed 12 Feb 2016
D.E. Goldberg, K. Deb, A comparative analysis of selection schemes used in genetic algorithms. Found. Genet. Algorithms 1, 69–93 (1991)
Z. Wu, J. Zhang, Y. Gao, An attribute reduction algorithm based on genetic algorithm and discernibility matrix. J. Softw. 7(11), 2640–2648 (2012)
S. Vijayabalaji, A. Ramesh, A new decision making theory in soft matrices. Int. J. Pure Appl. Math. 86(6), 927–939 (2013)
L. Chen, H. Liu, Z. Wan, An attribute reduction algorithm based on rough set theory and an improved genetic algorithm. J. Softw. 9(9), 2276–2282 (2014)
T. Herawan, A.N.M. Rose, M.M. Deris, Soft set theoretic approach for dimensionality reduction, in Database Theory and Application (Springer, Berlin, Heidelberg, 2009), pp. 171–178
J. Zhang, H.S.H. Chung, W.L. Lo, Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Trans. Evol. Comput. 11(3), 326–335 (2007)
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Appendix: Empirical Results
Appendix: Empirical Results
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Ezugwu, A.E., Shahbazova, S.N., Adewumi, A.O., Junaidu, S.B. (2018). Resource Selection with Soft Set Attribute Reduction Based on Improved Genetic Algorithm. In: Zadeh, L., Yager, R., Shahbazova, S., Reformat, M., Kreinovich, V. (eds) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-319-75408-6_16
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DOI: https://doi.org/10.1007/978-3-319-75408-6_16
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