Discrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational Science2658-46702658-7149Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University)2614010.22363/2658-4670-2021-29-1-53-62ArticlesСтатьиResearch ArticleOptimization of mobile device energy consumption in a fog-based mobile computing offloading mechanismОптимизация энергопотребления мобильных устройств в системе туманных вычисленийDaraseliyaAnastasia V.ДараселияА. В.

PhD student of Department of Applied Probability and Informatics

avdaraseliya@sci.pfu.edu.ruSopinEduard S.СопинЭ. С.

Candidate of Physical and Mathematical Sciences, Assistant professor of Department of Applied Probability and Informatics of Peoples’ Friendship University of Russia (RUDN University); Senior Researcher of Institute of Informatics Problems of Federal Research Center “Computer Science and Control” Russian Academy of Sciences

sopin-es@rudn.ruPeoples’ Friendship University of Russia (RUDN University)Российский университет дружбы народовFederal Research Center “Computer Science and Control” of the Russian Academy of Sciences (FRC CSC RAS)Федеральный исследовательский центр «Информатика и управление» РАН30032021291VOL 29, NO1 (2021)ТОМ 29, №1 (2021)536230032021Copyright ©; 2021, Daraseliya A.V., Sopin E.S.Copyright ©; 2021, Дараселия А.В., Сопин Э.С.2021Daraseliya A.V., Sopin E.S.Дараселия А.В., Сопин Э.С.http://creativecommons.org/licenses/by/4.0https://journals.rudn.ru/miph/article/view/26140

The offloading of computing tasks to the fog computing system is a promising approach to reduce the response time of resource-greedy real-time mobile applications. Besides the decreasing of the response time, the offloading mechanisms may reduce the energy consumption of mobile devices. In the paper, we focused on the analysis of the energy consumption of mobile devices that use fog computing infrastructure to increase the overall system performance and to improve the battery life. We consider a three-layer computing architecture, which consists of the mobile device itself, a fog node, and a remote cloud. The tasks are processed locally or offloaded according to the threshold-based offloading criterion. We have formulated an optimization problem that minimizes the energy consumption under the constraints on the average response time and the probability that the response time is lower than a certain threshold. We also provide the numerical solution to the optimization problem and discuss the numerical results.

Выгрузка задач мобильных вычислений в систему туманных вычислений представляется многообещающим подходом для снижения времени отклика ресурсоёмких мобильных приложений, функционирующих в режиме реального времени. Помимо снижения времени отклика, механизмы выгрузки вычислений помогут также снизить энергопотребление мобильных устройств. В этой статье мы проводим анализ энергопотребления мобильных устройств, которые используют инфраструктуру туманных вычислений для повышения производительности и увеличения времени их автономной работы. Рассматривается трёхуровневая вычислительная система, состоящая из непосредственно мобильного устройства, узла системы туманных вычислений и удалённого облака. Задачи мобильных вычислений могут быть обработаны локально на устройстве или быть выгружены в соответствии с пороговым критерием выгрузки. Сформулирована и решена задача оптимизации энергопотребления при наличии ограничений на среднее время отклика и на вероятность того, что время отклика ниже определённого порога.

queuing systemfog computingcloud computingqueuing theoryoptimizationLaplace-Stieltjes transformсистема массового обслуживаниятуманные вычисленияоблачные вычисленияоптимизацияпреобразование Лапласа-СтилтьесаThis paper has been supported by the RUDN University Strategic Academic Leadership Program (recipient Sopin E., mathematical model development). The reported study was funded by RFBR, project number 20-07-01052 (recipient Daraseliya A., optimization problem). The reported study was funded by RFBR, project number 19-07-00933 (recipient Sopin E., numerical analysis).M. Chiang and T. Zhang, “Fog and IoT: an overview of research opportunities,” IEEE Internet of Things Journal, vol. 3, no. 6, pp. 854-864, 2016. DOI: 10.1109/JIOT.2016.2584538.Z. Chang, Z. Zhou, T. Ristaniemi, and Z. Niu, “Energy efficient optimization for computation offloading in fog computing system,” in GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017, pp. 1-6. DOI: 10.1109/GLOCOM.2017.8254207.Y. Jiang, Y. Chen, S. Yang, and C. Wu, “Energy-efficient task offloading for time-sensitive applications in fog computing,” IEEE Systems Journal, vol. 13, no. 3, pp. 2930-2941, 2019. DOI: 10.1109/JSYST.2018.2877850.Q. Li, J. Zhao, Y. Gong, and Q. Zhang, “Energy-efficient computation offloading and resource allocation in fog computing for Internet of Everything,” China Communications, vol. 16, no. 3, pp. 32-41, 2019. DOI: 10.12676/j.cc.2019.03.004.E. S. Sopin, A. V. Daraseliya, and L. M. Correia, “Performance analysis of the offloading scheme in a fog computing system,” in 2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2018, pp. 1-5. DOI: 10.1109/ICUMT. 2018.8631245.E. Sopin, K. Samouylov, and S. Shorgin, “The analysis of the computation offloading scheme with two-parameter offloading criterion in fog computing,” pp. 11-20, 2019. DOI: 10.1007/978-3-030-34914-1_2.E. Sopin, N. Zolotous, K. Ageev, and S. Shorgin, “Analysis of the response time characteristics of the fog computing enabled real-time mobile applications,” Lecture Notes in Computer Science, vol. 12525, pp. 764-779, 2020. DOI: 10.1007/978-3-030-65726-0_9.