Received 18.08.2021; Revised 09.09.2021; Accepted 28.09.2021

Published 30.09.2021; First Online 25.10.2021

https://doi.org/10.34229/2707-451X.21.3.5

Previous  |  Full text (in Ukrainian)  |  Next

 

MSC 90C90, 90B18

Decision Making Models on the Market of Cloud Services

Vasyl Gorbachuk ^* ,   Maksym Dunaievskyi,   Seit-Bekir Suleimanov,   Lyudmyla Batih,   Denys Symonov

V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, Kyiv

^* Correspondence: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Introduction. Optimization can be applied in developing profitability management tools for a cloud service broker working according to a certain business model. On behalf of the managing telecommunications holding company (telecommunications operator), this broker integrates, aggregates and configures software and data storage services of third-party Internet software vendors. Such a broker receives only fixed commissions from this company, based on the subscription fee, but does not pay royalties to an Internet software vendor and does not receive payments from the sale of service packages.

The purpose. The cloud broker faces the problem of limited human resources required to carry out the relevant legal, technical and economic activities. In addition, the broker faces the problem of uncertainty in sales, service prices, the share of resource use, or the risk of losing operational and financial goals.

Results. To run a brokerʼs business efficiently, one needs to find services and their bundles that increase profitability and reduce financial risk by solving certain optimization problems. Information on such services is needed to support negotiations on fixed and variable commissions, as well as to prioritize services and their packages to be provided. Thus, for the cloud services broker, both profitability management tools and services portfolio development tools are useful. In general, a cloud service broker is an organization that negotiates the relationships between cloud service clients and Internet software vendors. Cloud broker can be created on the basis of different business models regarding the type of service (platform, infrastructure, software), type of clients (enterprise, household), functions performed (identity management, accounting, billing, location, etc.), the degree of rebranding, measures of aggregation of services and other criteria.

Conclusions. Different cloud brokers have different attitudes to choice of important solutions for their businesses. Solutions can relate to pricing, capacity planning and utilization in combination with service quality, security, scalability and other issues.

 

Keywords: optimization, portfolio, uncertainty, Boolean variables, revenue generation.

 

Cite as: Gorbachuk V., Dunaievskyi M., Suleimanov S., Batih L., Symonov D. Decision Making Models on the Market of Cloud Services. Cybernetics and Computer Technologies. 2021. 3. P. 53–64. (in Ukrainian) https://doi.org/10.34229/2707-451X.21.3.5

 

References

           1.     Gorbachuk V., Gavrilenko S., Golotsukov G., Dunaievskyi M. Principles for development of cloud technologies. Information Technologies and Computer Modelling. Ivano-Frankivsk: Vasyl Stefanyk Precarpathian National University, 2020. P. 82–83. (In Ukrainian). http://itcm.comp-sc.if.ua/2020/zbirnyk2020.pdf

           2.     Gorbachuk V., Gavrilenko S. The impact of cloud services pricing on provider profit, consumer surplus, and social welfare. Problems in Programming. 2020. 2−3. P. 237−245. (In Ukrainian). https://doi.org/10.15407/pp2020.02-03.237

           3.     Gorbachuk V., Gavrilenko S., Dunaievskyi M. To participation of Ukraine in the European Open Science Cloud. Global and Regional Problems of Informatization in Society and Nature Using. Kyiv: National University of Life and Environmental Sciences of Ukraine, 2021. P. 169–171. (In Ukrainian). https://www.researchgate.net/publication/352173161_To_participation_of_Ukraine_in_the_European_Open_ScienceCloud

           4.     Gorbachuk V., Gavrilenko S., Golotsukov G., Nikolenko D. Economics of Internet applications and digital content. The role of technology in the socio-economic development of the post-quarantine world. M.Gavron-Lapuszek, A.Karpenko (eds.) Katowice: Katowice School of Technology, 2020. P. 81–88. (In Ukrainian). https://www.researchgate.net/publication/345434136_Economics_of_internet_applications_and_digital_content

           5.     Gorbachuk V., Dunaievskyi M., Syrku A. Epidemic effects in network industries. International Conference on Software Engineering (April 12−14, 2021, Kyiv). Kyiv: National Aviation University, 2021. P. 68−72. https://www.researchgate.net/publication/353550873_Epidemic_effects_in_network_industries

           6.     Gorbachuk V.M. Post-industrial organization of government procurements in development of AUTODIN, ARPANET, PRNET, NSFNET and Internet. (In Ukrainian). Odessa National University Herarld, Economy, 2016. 21 (8). P. 116–122. http://visnyk-onu.od.ua/journal/2016_21_8/24.pdf

           7.     Kleinrock L. Kommunikatsionnye seti [Communication Nets. Stochastic Message Flow and Delay]. Moscow: Nauka, 1970. 256 p. (In Russian). https://www.amazon.com/Communication-Nets-Stochastic-Message-Engineering/dp/0486458806

           8.     Queueing Systems. Volume I: Theory Teoriya massovogo obsluzhivaniya [Queueing Systems. Volume I: Theory]. Moscow: Mashinostroenie, 1979. 432 p. (In Russian). https://www.wiley.com/en-us/Queueing+Systems%2C+Volume+I-p-9780471491101

           9.     Kleinrock L. Vychislitelʼnye sistemy s ocheredyami. [Queueing Systems. Volume II: Computer Applications]. Moscow: Mir, 1979. 600 p. (In Russian). https://www.wiley.com/en-us/Queueing+Systems%2C+Volume+2%3A+Computer+Applications-p-9780471491118

       10.     Nesse P.J., Svaet S., Strasunskas D., Gaivoronski A. A. Assessment and optimisation of business opportunities for telecom operators in the cloud value network. Transactions of Emerging Telecommunications Technologies. 2013. 24 (5). P. 503−516. https://doi.org/10.1002/ett.2666

       11.     Gaivoronski A.A., Strasunskas D., Nesse P.J., Svaet S., Su X. Modeling and economic analysis of the cloud brokering platform under uncertainty: choosing a risk/profit trade-off. Service Science. 2013. 5 (2). P. 137−162. https://doi.org/10.1287/serv.2013.0047

       12.     Wang X., Wu S., Wang K., Di S., Jin H., Yang K., Ou S. Maximizing the profit of cloud broker with priority aware pricing. 2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS) (December 15−17, 2017, Shenzhen). Shenzhen, China: IEEE, 2017. P. 511−518. https://doi.org/10.1109/ICPADS.2017.00073

       13.     Becker D.M., Gaivoronski A.A., Nesse P.J. Optimization based profitability management tool for cloud broker. Transactions in Emerging Telecommunications Technologies. 2019. 30 (1). https://doi.org/10.1002/ett.3527

       14.     Gaivoronski A.A., Lisser A., Lopez R., Xu H. Knapsack problem with probability constraints. Journal of Global Optimization. 2010. 49 (3). P. 397−413. https://doi.org/10.1007/s10898-010-9566-0

       15.     Zenios S.A. Practical Financial Optimization. Decision Making for Financial Engineers. Cambridge, MA: Blackwell, 2008. 430 p. https://www.wiley.com/en-us/Practical+Financial+Optimization%3A+Decision+Making+for+Financial+Engineers-p-9781405132015

       16.     Gaivoronski A.A., Zoric J. Evaluation and design of business models for collaborative provision of advanced mobile data services: a portfolio theory approach. Telecommunications Modeling, Policy, and Technology. S.Raghavan, B.Golden, E.Wasil (eds.) New York, NY: Springer, 2008. P. 353−386. https://doi.org/10.1007/978-0-387-77780-1_17

       17.     Laptin Yu.P., Bardadym T.O., Lefterov A.V. Optimization problems of document processing management. Cybernetics and Computer Technologies. 2020. 3. P. 5−13. (In Ukrainian). https://doi.org/10.34229/2707-451X.20.3.1

       18.     Rockafellar R.T., Uryasev S. Optimization of conditional value-at-risk. Journal of Risk. 2000. 2 (3). P. 21−41. https://doi.org/10.21314/JOR.2000.038

       19.     Shang D., Kuzmenko V., Uryasev S. Cash flow matching with risks controlled by buffered probability of exceedance and conditional value-at-risk, Annals of Operations Research. 2018. 260. P. 501–514. https://doi.org/10.1007/s10479-016-2354-6

       20.     Bardadym T., Gorbachuk V., Novoselova N., Osypenko S., Skobtsov V., Tom I. On biomedical computations in cluster and cloud environment. Cybernetics and Computer Technologies. 2021. 2. P. 76−84. https://doi.org/10.34229/2707-451X.21.2.8

       21.     Gorbachuk V., Ermoliev Y., Zagorodniy A., Bogdanov V., Ermolieva T., Rovenskaya E., Komendantova N., Borodina O., Knopov P., Norkin V., Gaivoronski A. Iterative Stochastic Quasigradient procedures for robust estimation, machine learning and decision making problems. 31-st European Conference on Operational Research (July 11−14, 2021, Athens, Greece). The Association of European Operational Research Societies, 2021. P. 184−185. https://www.researchgate.net/publication/353317157_Iterative_Stochastic_Quasigradient_procedures_for_robust_estimation_machine_learning_and_decision_making_problems

 

 

ISSN 2707-451X (Online)

ISSN 2707-4501 (Print)

Previous  |  Full text (in Ukrainian)  |  Next