Abstract
In cloud computing due to the multi-tenancy of the resources, there is an essential need for effective load management to ensure an efficient load sharing. Depends on the structure of the tasks, different algorithms could be applied to distribute the load. Workflow scheduling as one of those load distribution algorithms, is specifically designed to schedule the dependent tasks on available resources. Considering a job as an elastic network of dependent tasks, this paper describes how evolutionary algorithm, with its mathematical apparatus, could be applied as workflow scheduling in cloud computing. In this research, the impact of Generalized Spring Tensor Model on workflow load balancing, in context of mathematical patterns have been studied. This research can establish patterns in cloud computing which can be applied in designing the heuristic workflow load balancing algorithms to identify the load patterns of the cloud network. Furthermore, the outcome of this research can help the end users to recognize the threats of tasks failure in processing the e-business and e-since data in cloud environment.
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References
Khiyaita, A., Zbakh, M., El Bakkali, H., El Kettani, D.: Load balancing cloud computing: state of art. In: Network Security and Systems (JNS2), pp. 106–109 (2012)
Sawant, S.: A genetic algorithm scheduling approach for virtual machine resources in a cloud computing environment (2011)
Vöckler, J.-S., Juve, G., Deelman, E., Rynge, M., Berriman, B.: Experiences using cloud computing for a scientific workflow application. Condor 300, 15–24 (2011)
Zhang, C., De Sterck, H., Jaatun, M., Zhao, G., Rong, C.: CloudWF: a computational workflow system for clouds based on Hadoop. Cloud Comput. 5931, 393–404 (2009)
Galante, G., de Bona, L.C.E.: A survey on cloud computing elasticity. In: IEEE Fifth International Conference on Utility and Cloud Computing (UCC), pp. 263–270 (2012)
Lin, T.-L., Song, G.: Generalized spring tensor models for protein fluctuation dynamics and conformation changes. BMC Struct. Biol. 10(Suppl. 1), S3 (2010)
HowStuffWorks ‘Elasticity’: http://science.howstuffworks.com/dictionaryphysics-terms/elasticity-info.htm
Hookes Law elasticity limitation: http://www.clickandlearn.org/physics/sph4u/hookeslaw.htm
Bahar, I., Rader, A.J.: Coarse-grained normal mode analysis in structural biology. Curr. Opin. Struct. Biol. 15(5), 586–592 (2005)
Atilgan, A.R., Durell, S.R., Jernigan, R.L., Demirel, M.C., Keskin, O., Bahar, I.: Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys. J. 80(1), 505–515 (2001)
Relation, S.: Generalized Hooks Law (2009)
Yang, G., Kabel, J., Rietbergen, B.V.A.N., Odgaard, A., Huiskes, R.I.K., Cowin, S.C.: The Anisotropic Hooke’s law for cancellous bone and wood. J. Elast. 2138, 125–146 (1999)
Aweya, J., Ouellette, M., Montuno, D.Y., Doray, B., Felske, K.: An adaptive load balancing scheme for web servers. Int. J. Netw. Manag. 12(1), 3–39 (2002)
Gaussian network model—Wikipedia, the free encyclopedia: http://en.wikipedia.org/wiki/Gaussiannetworkmodel
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Aslanzadeh, S., Chaczko, Z., Chiu, C. (2015). Cloud Computing—Effect of Evolutionary Algorithm on Load Balancing. In: Borowik, G., Chaczko, Z., Jacak, W., Łuba, T. (eds) Computational Intelligence and Efficiency in Engineering Systems. Studies in Computational Intelligence, vol 595. Springer, Cham. https://doi.org/10.1007/978-3-319-15720-7_16
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DOI: https://doi.org/10.1007/978-3-319-15720-7_16
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