ABOUT THE MODEL OF ELECTRICITY CONSUMPTION BY A COMPANY THAT PROVIDES CLOUD RESOURCES
https://doi.org/10.32324/2412-8945-2021-1-40-44
Abstract
Modern mobile devices widely use cloud technologies. A cloud environment includes one or more data centers. As the number of mobile devices and their software increases, so does the number of servers and other hardware in cloud data centers. This increase leads to an increase in electricity consumption and related costs. Issues related to the energy management of cloud resources are important both from the point of view of protecting the environment and from the point of view of maximizing the profit from the provision of remote computing resources.
In this paper, we propose a stochastic model that allows us to estimate the amount of electricity consumed by comp anies providing services related to remote digital data processing. This model can also be used to simulate the behavior of such companies.
About the Authors
A. V. Logachov
Sobolev Institute of Mathematics SB RAS; Siberian State University of Geosystems and Technologies
Russian Federation
Russian Federation
Artem V. LOGACHOV — Candidate of Physical and Mathematical Sciences, Associate Professor, Senior Researcher, Professor
O. M. Logachova
Siberian State University of Geosystems and Technologies
Russian Federation
Russian Federation
Olga M. LOGACHOVA — Candidate of Physical and Mathematical Sciences, Associate Professor
References
1. Gnedenko B.V., Beljaev Ju.K., Solov'ev A.D. Matematicheskie metody v teorii nadezhnostij. Moscow, 1965.
2. A view of cloud computing / M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica et al. Communications of the ACM. 2010. Vol. 53, N 4, pp. 50—58.
3. Asad Z., Chaudhry M.A.R. A two-way street: Green big data processing for a greener smart grid. IEEE Systems Journal. 2017. N 11(2), pp. 784—795.
4. Beloglazov A., Buyya R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput Pract Exp. 2012. N 24(13), pp. 1397—1420.
5. Fan X., Weber W-D., Barroso L.A. Power Provisioning for a Warehouse-Sized computer. Proceedings of the 34th annual international symposium on computer architecture, ACM. 2007, pp. 13—23.
6. Huang J., Wu K., Moh M. Dynamic Virtual Machine Migration Algorithms Using Enhanced Energy Consumption Model for Green Cloud Data Centers. International Conference on High Performance Computing & Simulation (HPCS), 2014, pp. 902—910 (2020).
7. Power and performance management of virtualized computing environments via lookahead control / D. Kusic, J.O. Kephart, J.E. Hanson, N. Kandasamy, G. Jiang. Clust Comput. 2009. N 12(1), pp. 1—15.
8. Virtual machine based energy-efficient data center architecture for cloud computing: A performance perspective / K. Ye, D. Huang, X. Jiang, H. Chen, S. Wu. Proceedings of the 2010 IEEE/ACM International Conference on Green Computing and Communications. 2010, pp. 171—178.
9. Saadi Y., El Kafhali S. Energy-efficient strategy for virtual machine consolidation in cloud environment. Soft Comput. 2020. N 24(2), pp. 14845—14859.
10. Shi Y., Jiang X., Ye K. An Energy-Efficient Scheme for Cloud Resource Provisioning Based on CloudSim. IEEE International Conference on Cluster Computing, Austin, TX, USA, 2011, pp. 595—599.
Review
For citations:
Logachov A.V., Logachova O.M. ABOUT THE MODEL OF ELECTRICITY CONSUMPTION BY A COMPANY THAT PROVIDES CLOUD RESOURCES. Territory Development. 2021;(1 (23)):40-43. (In Russ.) https://doi.org/10.32324/2412-8945-2021-1-40-44
Views:
126
Email this article 