An Approach to Estimating Cloud Resources Prior to New Business Startup
One of the challenging issues in cloud computing is to estimate plausible cloud resources earlier to begin a new business. Vendors or cloud partners are often falling in risks by investing money without aware of the resources they can avail against their budget. In this study, we aim to build a two-stage model that guides vendors to specify a budget as a model parameter and obtain the possible cloud resources via a methodical process. In the first stage of the model, we consider and simulate the monthly cost calculator of Amazon EC2 service using Row Echelon Form to know which factors are involved in driving total cost for cloud resources and how much they affect the cost. In the second stage, we follow a reverse process to optimize the factor values using Genetic Algorithms for determining cloud resources. Finally, our proposed system is evaluated based on popular error measurement processes, MAE and MRE, and they show the outcomes are significant with moderate results in few cases.
L. J. Zhang and Q. Zhou. “CCOA: Cloud computing open architecture,” in IEEE International Conference on Web Services, 2009, pp. 607-616.
R. Buyya, C.S. Yeo, and S. Venugopal. "Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities," in 10th IEEE International Conference on High Performance Computing and Communications, 2008, pp. 5 - 13.
R Buyya, H. Stockinger, J. Giddy, D. Abramson. “Economic models for resource management and scheduling in grid computing,” Concurrency and Computation: Practice and Experience, vol. 14, pp. 1507-1542, Dec. 2002.
G.A. Thanos, C. Courcoubetis, G.D. Stamoulis. “Adopting the Grid for Business Purposes: The Main Objectives and the Associated Economic Issues,” in International Workshop on Grid Economics and Business Models, 2007, pp. 1 - 15.
J. Hwang, J. Park. “Decision Factors of Enterprises for Adopting Grid Computing,” in International Workshop on Grid Economics and Business Models, 2007, pp. 16 – 28.
J. Gray. “Distributed Computing Economics,” in Queue 6.3, 2008, pp. 63-68.
D. Kondo, B. Javadi, P. Malecot. “Cost-Benefit Analysis of Cloud Computing versus Desktop Grids,” in IEEE International Symposium on Parallel & Distributed Processing, 2009, pp. 1 - 12.
“Amazon EC2 simple monthly calculator.” Internet:
https://calculator.s3.amazonaws.com/index.html, [Sep. 13, 2016].
“Amazon Elastic Compute Cloud.” Internet: https://aws.amazon.com/ec2/, [Sep. 13, 2016].
P. Reed, B.S. Minsker, D.E. Goldberg. “A multiobjective approach to cost effective long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with historical data,” Journal of Hydroinformatics, vol. 3, pp. 71-89, 2002.
P.F. Pai, W.C. Hong. “Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms,” Electric Power Systems Research, vol. 74, pp. 417-425, Jun. 2005.
Y. Gao, H. Rong, J.Z. Huang. “Adaptive grid job scheduling with genetic algorithms,” Future Generation Computer Systems, vol. 21, pp. 151-161, Jan. 2005.
H.K.N. Leung, L. White. “A cost model to compare regression test strategies,” in Proc. IEEE Conference on Software Maintenance, 1991, pp. 201-208.
M.R. Young, R. Martin. “A minimax portfolio selection rule with linear programming solution,” Management Science, vol. 44, pp. 673-683, 1998.
T. Chai, R.R. Draxler. “Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature,” Geoscientific Model Development, vol. 7, pp. 1247-1250, 2014.
S. J. Leon. (1980). Linear algebra with applications. (8th edition). [On-line]. Available: http://www.academia.edu/download/36340306/Linear_Algebra_with_Applications_-_8th_-_Leon.pdf [Aug 05, 2016].
“Linear Algebra / Row Reduction and Echelon Forms.” Internet: https://en.wikibooks.org/wiki/Linear_Algebra/Row_Reduction_and_Echelon_Forms, [Aug. 05, 2016].
D. E. Goldberg. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. (1st edition).
“Genetic Algorithm.” Internet: https://www.mathworks.com/discovery/genetic-algorithm.html, [Aug. 13, 2016].
K. Deb. “An efficient constraint handling method for genetic algorithms,” Computer Methods in Applied Mechanics and Engineering, vol. 186, pp. 311-338, 2000.
Y. Ge, G. Wei. “GA-based task scheduler for the cloud computing systems,” in IEEE International Conference on Web Information Systems and Mining (WISM), 2010, pp. 181-186.
K. Dasgupta, B. Mandal, P. Dutta, et al. “A genetic algorithm (ga) based load balancing strategy for cloud computing,” in International Conference on Computational Intelligence Modeling Techniques and Applications, 2013, pp. 340-347.
“Find the matrix in reduced row echelon form - Linear Algebra Toolkit.” Internet: www.math.odu.edu/~bogacki/cgi-bin/lat.cgi?c=rref, [Aug. 6, 2016].
“Constrained Minimization Using the Genetic Algorithm.” Internet: https://www.mathworks.com/help/gads/examples/constrained-minimization-using-the-genetic-algorithm.html, [Aug. 15, 2016].
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