The Presentation of a Solution to Optimize the Time and Cost of Software Component Placement Using a Multi-Objective Meta-Exploratory Algorithm in the Cloud Environment
Assistant Professor, facuty of Engineering, Qom Branch,Islamic Azad university,Qom, Iran
Abstract
In the last decade, cloud computing has attracted the attention of many IT providers and users. One of the most widely used models of providing services in the field of cloud computing is the “software as a service” or SaaS model, which is usually provided as a combination of data and application components. One of the major challenges in this area is finding the optimal location for the software components on the cloud infrastructure where the software as a service can perform at its best. The problem of locating software as a service, addresses the challenge of determining which components in the cloud data center can host which components without violating the limitations of the software as a service. In this paper, we have presented a multi-objective optimization solution with the aim of reducing costs and execution time for locating components in cloud environments. We have simulated our proposed solution using the Cloudsim library and finally evaluated and compared it with two multi-objective and cuckoo search algorithms. The simulation results show that the proposed solution performs better than the two basic algorithms, reducing the implementation time of the “software as a service” components and the costs by 9.4% and 9.1% respectively, and increasing the productivity by 7.9%.
Mell, G. Timothy, “The NIST definition of cloud computing”, Technical report, National Institute of Standards and Technology, 2011.##
Chandrasekaran, “Essentials of cloud computing”, CrC Press, 2014.##
Ghafouri, “Multi-tenant survey in cloud computing environments”, Journal of Information Technology and Applied Communication Innovations, Vol.1, no. 1, 1398. (In Persian)##
Kumar, “Placement of software-as-a-service components in cloud computing environment” (Doctoral dissertation), 2014.##
Yusoh “ Composite SaaS resource management in cloud computing using evolutionary computation. PhD thesis”, Science and Engineering Faculty Queensland University of Technology Brisbane, Australia, 2013.##
Candan, Li. W-S, and T. Phan, M. Zhou “At the frontiers of information and software as services”, In: New Frontiers in information and software as services, Springer, pp. 283–300, 2011.##
Right Scale IRight scale 2016 state of the cloud report. Technical report, RightScale Inc.2016.##
Statista, “Software as a service (SaaS)”, subscription revenue from 2012 to 2016 by category (in billion u.s. dollars). http:// www.statista.com/statistics/468649/saas-software-subscriptionrevenue-by-category/. Accessed 22 March 2016.##
Cisco service-oriented network architecture: support and optimizesoaandweb2.0 Technicalreport, Cisco Inc. 2008.
TalbiE- ,G., BouvryP(2015)”A survey of evolutionary computation for resource management of processing incloud computing” [review article]. IEEE Comput Intell Mag 10(2):53–67. 2015.##
Rezaei and M. Ghobaei Arani, “An Approach Based on Multiobjective Genetic Algorithm (NSGA II) to placement Composite SaaS”, 4the National Conference on Technology in Electrical and Computer Engineering, 2018.(In Persian)##
Yusoh, and M. Tang, “A penalty-based genetic algorithm for the composite SaaS placement problem in the cloud”, In IEEE Congress on Evolutionary Computation, pp. 1-8, IEEE. 2010.##
Yusoh, and M. Tang, ”A cooperative coevolutionary algorithm for the composite SaaS placement problem in the cloud”, In International Conference on Neural Information Processing Springer, Berlin, Heidelberg, pp. 618-625, 2010.##
Yusoh, and Z. Izzah, “Composite SaaS resource management in cloud computing using evolutionary computation (Doctoral dissertation”, Queensland University of Technology, 2013.##
W. Ni, X.F. Pan, and Z. Wu, “An ant colony optimization for the composite SaaS placement problem in the cloud”, In Applied mechanics and materials Trans Tech Publications, Vol. 130, pp. 3062-3067, 2012.##
Bowen, and W. Shaochun, “An adaptive simulated annealing genetic algorithm for the data placement problem in SaaS.” In 2012 International Conference on Industrial Control and Electronics Engineering, pp. 1037-1043, IEEE, 2012.##
Liu, Z. Hu, and L. Jonepun, “Research on Composite SaaS Placement Problem Based on Ant Colony Optimization Algorithm with Performance Matching Degree Strategy”, Journal of Digital Information Management, Vol. 12, no. 4, 2014.##
Huang, and B. Shen, “Service deployment strategies for efficient execution of composite SaaS applications on cloud platform”, Journal of Systems and Software, Vol. 107, pp. 127-14, 2015.##
ajji, and H. Mezni, “A composite particle swarm optimization approach for the composite saas placement in cloud environment”, Soft Computing, Vol. 22, no. 12, pp. 4025-4045, 2018.##
Mezni, M. Sellami, and J. Kouki, “Security‐aware SaaS placement using swarm intelligence. Journal of Software”, Evolution and Process, Vol. 30, no. 8, p. 1932, 2018.##
Kumar, “Placement of software-as-a-service components in cloud computing environment”, (Doctoral dissertation), 2014.##
Altmann. and M. Kashef, “Cost model based service placement in federated hybrid clouds”, Future Generation Computer Systems, Vol. 41, pp. 79-90, 2014.##
Chainbi, And E. Sassi, “A multiswarm for composite SaaS placement optimization based on PSO. Software”, Practice and Experience, Vol. 48, no. 10, pp.1847-1864. 2018.##
Calheiros, N. Rodrigo, R. Ranjan, A. Beloglazov, César AF De Rose, and B. Rajkumar “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”, Software: Practice and experience, Vol. 41, no. 1, pp. 23-50, 2011.##
رضائی, ., & ghobaei, M. (2022). The Presentation of a Solution to Optimize the Time and Cost of Software Component Placement Using a Multi-Objective Meta-Exploratory Algorithm in the Cloud Environment. , 1(4), 19-33.
MLA
مریم رضائی; mostafa ghobaei. "The Presentation of a Solution to Optimize the Time and Cost of Software Component Placement Using a Multi-Objective Meta-Exploratory Algorithm in the Cloud Environment", , 1, 4, 2022, 19-33.
HARVARD
رضائی, ., ghobaei, M. (2022). 'The Presentation of a Solution to Optimize the Time and Cost of Software Component Placement Using a Multi-Objective Meta-Exploratory Algorithm in the Cloud Environment', , 1(4), pp. 19-33.
VANCOUVER
رضائی, ., ghobaei, M. The Presentation of a Solution to Optimize the Time and Cost of Software Component Placement Using a Multi-Objective Meta-Exploratory Algorithm in the Cloud Environment. , 2022; 1(4): 19-33.
)