Подходы к постановке задачи оптимизации распределения ресурсов в вычислительной сети
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Approaches to statement of the resource distribution optimization problem in a computer network

Rozhkova T.S.   Afanaciev V.V.   Vetrov I.I.  

UDC 004.75
DOI: 10.26102/2310-6018/2020.31.4.013

  • Abstract
  • List of references
  • About authors

The article discusses a distributed computing system, represented by a variety of mobile terminals, providing the ability to serve the requests of users of these terminals to run programs, the need for computing resources, which exceeds the local computing resources available on these terminals. This possibility is provided by the implementation of the cooperative computing paradigm, which supports the procedure for dynamic formation of the cooperative computing resource of a plurality of mobile terminals, taking into account the possibility of their disconnection and connection to the cooperative computing procedure. Using the set-theoretic representation, such parameters of the system functioning are determined as the response time of the node to the request for the provision of computing resources, as well as the delay period in the queue for requests belonging to different mobile terminals. On the basis of these parameters, for the specified conditions, an optimization problem of the cooperative use of computing resources is posed in a generalized form. The formulation of particular problems of optimization of computing resources for a system consisting of two mobile terminals is considered in detail, taking into account various conditions of their need for computing resources, as well as the current availability of computing resources in the nodes of the system. The approaches obtained as a result of the formulation of these particular problems are extended to a system consisting of many mobile terminals.

1. Liu L., Chen R., Geirhofer S., Sayana K., Shi Z., and Zhou Y. Downlink MIMO in LTEadvanced: SU-MIMO vs. MU-MIMO. IEEE Communications Magazine. 2012. Available from: https://ieeexplore.ieee.org/document/6146493 DOI: 10.1109/MCOM.2012.6146493 (Accessed: 10th November 2020).

2. Rangan S., Rappaport T. S., Erkip E. Millimeter-wave cellular wireless networks: Potentials and challenges Proceedings of the IEEE. 2014. Available from: https://arxiv.org/pdf/1401.2560.pdf DOI: 10.1109/JPROC.2014.2299397 (Accessed: 10th November 2020).

3. Atapattu S., Jing Y., Jiang H., Tellambura C. Relay selection and performance analysis in multiple-user networks. IEEE Journal on Selected Areas in Communications. 2013. Available from: https://arxiv.org/pdf/1110.4126.pdf DOI: 10.1109/JSAC.2013.130815 (Accessed:12th October 2020).

4. Atapattu S., Dharmawansa P., Di Renzo M., Tellambura C., Evans J. S. Multi-user relay selection for full-duplex radio. IEEE Transactions on Communications. 2019. Available from: https://hal.archives-ouvertes.fr/hal-02395808 DOI: 10.1109/TCOMM.2018.2877393 (Accessed: 10th November 2020).

5. Liu Z., Lin M., Wierman A., Low S. H., Andrew L.L.H. Greening geographical load balancing. IEEE/ACM Transactions on Networking. 2011. Available from: https://www.researchgate.net/publication/221596480_Greening_Geographical_Load_Bala ncing DOI: 10.1109/TNET.2014.2308295 (Accessed: 20th November 2020).

6. Farahnakian F., Liljeberg P., Plosila J. Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. 2014. Available from: https://ieeexplore.ieee.org/document/6787321 DOI: 10.1109/PDP.2014.109 (Accessed: 27th September 2020).

7. Chen X., Jiao L., Li W., Fu X. Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking. 2016. Available from: https://arxiv.org/pdf/1510.00888.pdf DOI: 10.1109/TNET.2015.2487344 (Accessed: 1st October 2020).

8. Rozhkova T.S. Podhody k ispol'zovaniju aukcionnyh metodov dlja upravlenija resursami v raspredelennoj vychislitel'noj sisteme. Materials of the international scientific and technical conference of students, graduate students and young scientists "Scientific session of TUSUR-2020". 2020;2:64-67.

9. Tompkins M.F. Optimization Techniques for Task Allocation and Scheduling in Distributed Multi-Agent Operations. Massachusetts Institute of Technology. 2003. Available from: https://core.ac.uk/download/pdf/4384944.pdf (Accessed: 17th November 2020).

10. Chen L., Zhou S., Xu J. Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks. IEEE/ACM Transactions on Networking. 2018. Available from: https://arxiv.org/pdf/1703.06058.pdf DOI: 10.1109/TNET.2018.2841758 (Accessed: 8th October 2020).

11. Mao Y., Zhang J., Song S.H., Letaief K.B. Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems. IEEE Transactions on Wireless Communications. 2017. Available from: https://ieeexplore.ieee.org/document/7956189 DOI: 10.1109/TWC.2017.2717986 (Accessed: 26th October 2020).

12. Boyd S. P., Vandenberghe L. Convex optimization. Cambridge University Press. 2004. Available from: https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf (Accessed: 7th. November 2020).

Rozhkova Tatyana Sergeevna

Email: lebedenko_eugene@mail.ru

The Federal Guard Service Academy

Orel, Russian Federation

Afanaciev Vadim Vladimirovich
Candidate of Technical Sciences
Email: lebedenko_eugene@mail.ru

The Federal Guard Service Academy

Orel, Russian Federation

Vetrov Igor Ivanovich

Email: lebedenko_eugene@mail.ru

The Federal Guard Service Academy

Orel, Russian Federation

Keywords: distributed computing system, fog computing, cooperative computing, resource allocation, parallelization of computing tasks, computing resource, node response time, cooperative computing network

For citation: Rozhkova T.S. Afanaciev V.V. Vetrov I.I. Approaches to statement of the resource distribution optimization problem in a computer network. Modeling, Optimization and Information Technology. 2020;8(4). Available from: https://moitvivt.ru/ru/journal/pdf?id=859 DOI: 10.26102/2310-6018/2020.31.4.013 (In Russ).

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