Development of the Model of Dynamic Storage Distribution in Data Processing Centers

Full Text (PDF, 568KB), PP.18-24

Views: 0 Downloads: 0


Rashid G. Alakbarov 1,* Fahrad H. Pashaev 2 Mammad A. Hashimov 1

1. Institute of Information Technology of ANAS, Baku, Azerbaijan

2. Institute of Control Systems after Academician A. Huseynov of ANAS, Baku, Azerbaijan

* Corresponding author.


Received: 21 Aug. 2014 / Revised: 3 Dec. 2014 / Accepted: 19 Jan. 2015 / Published: 8 Apr. 2015

Index Terms

Data Processing Center, Cloud Computing, Storage Capacity, Markov Process, Stochastic Model, Virtual Resource


The paper reviews dynamic distribution of storage resources among the users in data processing centers. The process of changing memory usage state was revealed to be the process of Markov. The paper proposes the development of stochastic model of the memory and computing usage distribution and the development of probability density functions over practical data. Parameters of probability density functions were defined with the help of stochastic model and practical data. The calculation of the developed model and the parameters of the probability density function is realized dynamically during the ongoing process. At the beginning of each time interval, it is forecasted that the process will be shifted to which state with which maximum probability. The adequacy of the previous forecasts is monitored. Note that, over the time, the quality of the forecast and the level of adequacy increases. The model is used in the virtualization of storage resources usage process and ensures the use of storage resources without wasting. Structure of visualization base is given. The base enables to monitor all stages of the process. Using monitoring base the issues can be resolved to analyze different aspects of the process. Recommendations are given on the use of obtained results.

Cite This Paper

Rashid G. Alakbarov, Fahrad H. Pashaev, Mammad A. Hashimov, "Development of the Model of Dynamic Storage Distribution in Data Processing Centers", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.5, pp.18-24, 2015. DOI:10.5815/ijitcs.2015.05.03


[1]Voevodin V.V., Voevodin Vl.V. Parallel computing. St. Petersburg. “BHV – Petersburg”, 2002.

[2]Ilin Yu. IBM invests “Cloud Computing”, -

[3]Alguliyev R.M., Alekperov R.K. Cloud Computing: Modern State, Problems and Prospects. Telecommunications and Radio Engineering, 2013, vol.72, no.3, pp.255-266

[4]Marios D. Dikaiakos, George Pallis, Dimitrios Katsaros, Pankaj Mehra, Vakali Athena. Cloud Computing, Distributed Internet Computing for IT and Scientific Research // IEEE INTERNET COMPUTING - 2009. № 9. P. 10-13.

[5]Keedong Yoo. Cloud Storage-based Intelligent Document Archiving for the Management of Big Data. International Journal of Information Technology & Computer Science ( IJITCS ) (ISSN No : 2091-1610 ) Volume 9 : IssueNo : 3 : Issue on May /June, 2013.

[6]Rajendar Kandan, Mahendran Ellappan, Madhusudhana Rao S & Rajagopalan M.R. Dynamic Resource Provisioning Framework (DRPF) using Gridand Cloud Computing. International Journal of Information Technology & Computer Science (IJITCS) ( (ISSN : 2091-1610 ) , Volume No : 12 , IssueNo : 3 .

[7]A.A. Markov. "Extension of the limit theorems of probability theory to a sumofvariables connected in a chain". Reprintedin Appendix B of: R. Howard. Dynamic Probabilistic Systems, volume 1: Markov Chains. John Wileyand Sons, 1971

[8]Sean Meyn. Control Techniques for Complex Networks. Cambridge University Press. 2007, p.615.

[9]S.P. Meyn and R.L.Twedie. Marcov Chains and Stochostic Stability. Springer-Verlag 1993, p 552.

[10]Hemdi A. Taha, Introduction to Operations Research, 7th Edition. Hardcover, from English. / Hemdi A. Taha Publishing House "Williams", 2005. -912 p.

[11]Fu and Koutras, M. J. C. V. Distribution theory of runs: A Markov chain approach // Journal of the American Statistical Assocation. V.89.-1996. P.1050-1058.

[12]Balasubramanian K., Viveros R. and Balakrishnan N. Sooner and later waiting time problems for Markovian Bernoulli trials, statistically // Probab. Lett. - 1993. P.153-161.

[13]Kolev N. and Minkov L. On the joint distribution of the successes and failures related to success runs of length k in the homogeneous Markov chain // Compt.Randue Bulg . Acad. Sci. , 48. –1995. V.9. 19-22.

[14]Graham Ronald, Knuth Donald, Patashnik Oren. “(5) Binomial Coefficients” Concrete Mathematics (2nd ed.) // Addison Wesley. 1994. PP. 153-256. ISBN 0-201-55802-5. OCLC 17649857.

[15]Shilov, G. E. (1977). Linear algebra. Dover Publications.ISBN 978-0-486-63518-7.

[16]Cristian Walck. Hand-book on Statistical distributions for experimentalists. Practicle Physics Group Fysikum, University of Stoskholm, 2007, p.2002.

[17]Gamma Function and Related Functions by Philip J. Davis in Handbook of Mathematical Functions, ed. M. Abramowitz and I. A. Stegun, Dover Publications, Inc., New York, 1965, p.253.

[18]Alakbarov R.M., Guseinova A.A. Development of adaptive model for optimal load distribution in corporate networks. //Telecommunications. 2012. № 9.P.17-21.

[19]Sutcliffe A.G., 1995, Human-Computer Interface Design (2nd Ed), MacMillan, pp. 274-276.

[20]Dix et al, 1998, Human-Computer Interaction (2nd Ed), Prentice Hall, pp. 212-220.