FEATURES OF THE DISTRIBUTION OF COMPUTING RESOURCES IN CLOUD SYSTEMS

Authors

  • Inna Petrovska
  • Heorhii Kuchuk

DOI:

https://doi.org/10.26906/SUNZ.2022.2.075

Keywords:

cloud technologies, cloud resources, optimal allocation of resources

Abstract

The article considers the classification of existing technologies for providing cloud services. Advantages and disadvantages are determined for each technology. Characteristic features of cloud computing, which must be taken into account when allocating resources, are also defined. It was determined that all technologies take into account only the necessary amounts of processing resources, RAM and storage space. At the same time, the specifics of the applications are not taken into account. It also does not take into account the sharing of resources between different applications. Thus, it is not always possible to choose the optimal resource for placing customer applications. This often leads to a significant decrease in application performance. In addition, the cloud resource is used inefficiently. Existing approaches and methods of resource allocation do not take into account all the features of cloud computing. This can lead to inefficient use of cloud infrastructure. Therefore, the purpose of this article is to determine the characteristic features of cloud computing, which will need to be taken into account when allocating resources. The task is to ensure an even distribution of the load on all cloud servers. As a result of the analysis, it was proved that the IAAS technology is the most vulnerable to the quality of resource allocation. The necessary set of indicators for IAAS technology, which must be taken into account when allocating resources, is determined.

Downloads

Download data is not yet available.

References

Zhen Xiao, Weija Song, and Qu Chen, “Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment”, IEEE transaction on parallel and distributed systems, Vol/ 24, Is. 6, pp. 1107 – 1117, June 2013, doi: 10.1109/TPDS.2012.283.v/

P. Franti, “Efficiency of random swap clustering”, Journal of Big Data, 2018, vol. 5, No. 13, pp. 1-29. doi: 10.1186/s40537-018-0122-y.

N. Kuchuk, O. Shefer, G. Cherneva, and F. A. Alnaeri, “Determining the capacity of the self-healing network segment”, Advanced Information Systems, vol. 5, no. 2, pp. 114–119, Jun. 2021, doi: 10.20998/2522-9052.2021.2.16.

Ye. Qiang, and W. Zhuang, “Distributed and adaptive medium access control for internet-of-things-enabled mobile networks”, IEEE Internet of Things Journal, 2017, vol. 4, no. 2, pp. 446-460, doi: 10.1109/JIOT.2016.2566659.

H. Khudov, K. Tahyan, V. Chepurnyi, I. Khizhnyak, K. Romanenko, A. Nevodnichii, and O. Yakovenko, “Optimization of joint search and detection of objects in technical surveillance systems”, Advanced Information Systems, 2020, Vol. 4, No. 2, pp. 156-162, doi: 10.20998/2522-9052.2020.2.23.

S. Semenov, and Cao Weilin, “Testing process for penetration into computer systems mathematical model modification”, Advanced Information Systems, Vol. 4, No. 3, pp. 133–138. 2020, doi: 10.20998/2522-9052.2020.3.19.

G. Kuchuk, A. Kovalenko, I.E. Komari, A. Svyrydov, and V. Kharchenko, “Improving big data centers energy efficiency: Traffic based model and method”, Studies in Systems, Decision and Control, vol. 171, Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (Eds.), Springer Nature Switzerland AG, pp. 161-183, 2019, doi: 10.1007/978-3-030-00253-4_8.

A. Nechausov, I. Mamusuĉ, and N. Kuchuk, “Synthesis of the air pollution level control system on the basis of hyperconvergent infrastructures”, Advanced Information Systems, vol. 1, no. 2, 2017, pp. 21–26. DOI: 10.20998/2522-9052.2017.2.04.

H. Kuchuk, A. Kovalenko, B.F. Ibrahim, and I. Ruban, “Adaptive compression method for video information”, International Journal of Advanced Trends in Computer Science and Engineering, 8(1), pp. 66–69, 2019, doi: http://dx.doi.org/10.30534/ijatcse/2019/1181.22019.

S. Bulba, “Composite application distribution methods”, Advanced Information Systems”, vol. 2, no. 3, pp. 128–131, 2018, doi: 10.20998/2522-9052.2018.3.22.

Published

2022-06-07

Most read articles by the same author(s)