K.S. Myshenkov1, D.A. Gurianov2

1,2 Bauman Moscow State Technical University (Moscow, Russia)

The article discusses the advantages of the cloud model “infrastructure as a service”, a way to solve the problem of integrating an enterprise with a cloud infrastructure if a full transition to cloud computing in public cloud systems is unacceptable for various reasons. As an option to solve the problem, it is proposed to organize a hybrid cloud cluster from the existing equipment of the enterprise and the public cloud of one or more providers. The issue of building multi-provider cloud structures, the limitations of the current way of managing virtual machines, the features of organizing virtual cloud data centers and their structure are considered, a comparison is made of the classic model for deploying server applications with a model managed by a load balancer on virtual machines. As a means of organizing and managing a hybrid and complex cloud, the Nebula Cloud software package is proposed, its capabilities, structure, modules, technology and method of work, results of test implementations are described. The results of comparing the software package being developed with the closest competing analogues of large vendors are given, conclusions are made about the prospects for using the software package and plans for further research with the aim of increasing the speed, efficiency and fault tolerance of virtual machines.

Myshenkov K.S., Gurianov D.A. Program for organizing the work of hybrid and multi-provider cloud structures of the enterprise. Dynamics of complex systems. 2021. T. 15. № 4. Р. 44−53. DOI: 10.18127/j19997493-202104-06 (In Russian)

1. Gur'yanov D.A., Zelenskij A.A., Myshenkov K.S. Sistema upravleniya gibridnoj oblachnoj infrastrukturoj predpriyatiya // V kn.: E.P. Tkacheva (Ed.), Innovacionnye napravleniya issledovanij v sfere estestvennyh i tekhnicheskih nauk Belgorod: Agentstvo perspektivnyh nauchnyh issledovanij (APNI). S. 49–53 (In Russian). 
2. Klement'ev I.P., Ustinov V.A. Vvedenie v oblachnye vychisleniya. Saratov: Profobrazovanie. 2019 (In Russian).Afzal S., Kavitha G. Load balancing in cloud computing – A hierarchical taxonomical classification. Journal of Cloud Computing. 2019, December 1. Springer. https://doi.org/10.1186/s13677-019-0146-7
3. Alexander K., Lee C., Kim E., Helal S. Enabling End-To-End Orchestration of Multi-Cloud Applications. IEEE Access. 2017. № 5. P. 18862–18875. https://doi.org/10.1109/ACCESS.2017.2738658
4. Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., Ahmad, I. Cloud Computing Pricing Models: A Survey. International Journal of Grid and Distributed Computing. 2013 6(5), 93–106. https://doi.org/10.14257/ijgdc.2013.6.5.09
5. Beranek M., Kovar V., Feuerlicht G. Framework for Management of Multi-tenant Cloud Environments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).2018. V. 10967 LNCS. P. 309–322. Springer Verlag. https://doi.org/10.1007/978-3-319-94295-7_21
6. Boja C., Pocatilu P., Toma C. The Economics of Cloud Computing on Educational Services. Procedia – Social and Behavioral Sciences. 2013. V. 93. P. 1050–1054. https://doi.org/10.1016/j.sbspro.2013.09.328
7. Buyya R., Yeo C.S., Venugopal S., Broberg J., Brandic I. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems. 2009. V. 25(6). P. 599–616. https://doi.org/10.1016/j.future.2008.12.001
8. Dimitri N. Pricing cloud IaaS computing services. Journal of Cloud Computing. 2020. V. 9(1). P. 14. https://doi.org/10.1186/s13677-02000161-2
9. Durkee D. Why cloud computing will never be free. Communications of the ACM. 2010. V. 53(5). P. 62–69. https://doi.org/10.1145/1735223.1735242
10. Faragardi H.R., Saleh Sedghpour M.R., Fazliahmadi S., Fahringer T., Rasouli N. GRP-HEFT: A Budget-Constrained Resource Provisioning Scheme for Workflow Scheduling in IaaS Clouds. IEEE Transactions on Parallel and Distributed Systems. 2020.  V. 31(6). P. 1239–1254. https://doi.org/10.1109/TPDS.2019.2961098
11. Ferrer A.J., Pérez D.G., González R.S. Multi-cloud Platform-as-a-service Model, Functionalities and Approaches. In Procedia Computer Science 2016/ V. 97. P. 63–72. Elsevier B.V. https://doi.org/10.1016/j.procs.2016.08.281
12. González-Vélez H., Kontagora M. Performance evaluation of MapReduce using full virtualisation on a departmental cloud. International Journal of Applied Mathematics and Computer Science. 2011. V. 21(2). P. 275–284. https://doi.org/10.2478/v10006-0110020-3
13. Inokuchi K., Kourai K. Secure VM management with strong user binding in semi-trusted clouds. Journal of Cloud Computing. 2020. V. 9(1) P. 3. https://doi.org/10.1186/s13677-020-0152-9
14. Kritikos K., Zeginis C., Iranzo J., Gonzalez R.S., Seybold D., Griesinger F., Domaschka J. Multi-cloud provisioning of business processes. Journal of Cloud Computing. 2019. V. 8(1). P. 18. https://doi.org/10.1186/s13677-019-0143-x
15. Lee I. An optimization approach to capacity evaluation and investment decision of hybrid cloud: a corporate customer’s perspective. Journal of Cloud Computing. 2019. V. 8(1). P. 15. https://doi.org/10.1186/s13677-019-0140-0
16. Li L., Tang T., Chou W. A REST Service Framework for Fine-Grained Resource Management in Container-Based Cloud. In Proceedings – 2015 IEEE 8th International Conference on Cloud Computing, CLOUD 2015. 2015. P. 645–652. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CLOUD.2015.91
17. Li S., Xiao L., Shi C., Che L., Zhang C., Li Y. Boosting performance of virtualized desktop infrastructure with physical GPU and SPICE. Science China Information Sciences. 2020. July 1. Science in China Press. https://doi.org/10.1007/s11432-018-9914-5
18. Misra S.C., Mondal A. Identification of a company’s suitability for the adoption of cloud computing and modelling its corresponding Return on Investment. Mathematical and Computer Modelling. 2011. V. 53(3–4). P. 504–521. https://doi.org/10.1016/j.mcm.2010.03.037
19. Neghabi A.A., Navimipour N.J., Hosseinzadeh M., Rezaee A. Load Balancing Mechanisms in the Software Defined Networks: A Systematic and Comprehensive Review of the Literature. IEEE Access. Institute of Electrical and Electronics Engineers Inc. 2018, March 4. https://doi.org/10.1109/ACCESS.2018.2805842
20. Opara-Martins J., Sahandi R., Tian F. Critical analysis of vendor lock-in and its impact on cloud computing migration: a business perspective. Journal of Cloud Computing. 2016. V. 5(1). P. 4. https://doi.org/10.1186/s13677-016-0054-z
21. Pal R., Hui P. Economic models for cloud service markets: Pricing and Capacity planning. In Theoretical Computer Science. 2013. V. 496. P. 113–124. https://doi.org/10.1016/j.tcs.2012.11.001
22. Pradhan P., Behera P.K., Ray B.N.B. Modified Round Robin Algorithm for Resource Allocation in Cloud Computing. In Procedia Computer Science. 2016. V. 85. P. 878–890. Elsevier B.V. https://doi.org/10.1016/j.procs.2016.05.278
23. Randal A. The ideal versus the real: Revisiting the history of virtual machines and containers. ACM Computing Surveys. 2020, February 1. Association for Computing Machinery. https://doi.org/10.1145/3365199
24. Secinti C., Ovatman T. Fault Tolerant VM Consolidation for Energy-Efficient Cloud Environments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018. V. 10967 LNCS. P. 323–333. Springer Verlag. https://doi.org/10.1007/978-3-319-94295-7_22
25. Singh A., Juneja D., Malhotra M. Autonomous agent based load balancing algorithm in Cloud Computing. In Procedia Computer Science. 2015. V. 45. P. 832–841. Elsevier B.V. https://doi.org/10.1016/j.procs.2015.03.168
26. Soltani S., Martin P., Elgazzar K. A hybrid approach to automatic IaaS service selection. Journal of Cloud Computing. 2018.  V. 7(1). P. 12. https://doi.org/10.1186/s13677-018-0113-8
27. Tadokoro H., Kourai K., Chiba S. Preventing information leakage from virtual machines’ memory in IaaS clouds. IPSJ Online Transactions. 2012. V. 5(2012). P. 156–166. https://doi.org/10.2197/ipsjtrans.5.156
28. Vandebon J., Coutinho J.G.F., Luk W., Nurvitadhi E., Naik M. Enhanced Heterogeneous Cloud: Transparent Acceleration and Elasticity. 2020. P. 162–170. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/icfpt47387.2019.00027
29. Zhang Q., Cheng L., Boutaba R. Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications. 2010. V. 1(1). P. 7–18. https://doi.org/10.1007/s13174-010-0007-6