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)3220510.22363/2658-4670-2022-30-3-244-257ArticlesСтатьиResearch ArticleDevelopment and analysis of models for service migration to the MEC server based on hysteresis approachРазработка и анализ моделей гистерезисного управления миграцией сервисов на сервер граничных вычисленийhttps://orcid.org/0000-0002-4246-8483PoluektovDmitry S.ПолуэктовД. С.

postgraduate student of Department of Applied Probability and Informatics

poluektov-ds@rudn.ruhttps://orcid.org/0000-0003-2362-3270KhakimovAbdukodir A.ХакимовА. А.

Researcher of Department of Applied Probability and Informatics

khakimov-aa@rudn.ruPeoples’ Friendship University of Russia (RUDN University)Российский университет дружбы народов05102022303VOL 30, NO3 (2022)ТОМ 30, №3 (2022)24425705102022Copyright ©; 2022, Poluektov D.S., Khakimov A.A.Copyright ©; 2022, Полуэктов Д.С., Хакимов А.А.2022Poluektov D.S., Khakimov A.A.Полуэктов Д.С., Хакимов А.А.https://creativecommons.org/licenses/by-nc/4.0https://journals.rudn.ru/miph/article/view/32205

Online video services are among the most popular ways of content consumption. Video hosting servers have a very high load every day, which we propose to reduce by migrating the application with the video content in demand to the local Multi-access Edge Computing (MEC) server of the target. This makes it possible to improve the quality of services (QoS) provided to users by reducing the transmission delay. Therefore, an architecture has been proposed that allows, at times of increased demand for the same video content, to migrate the video service application to the edge servers of the network operator. To evaluate the performance of this approach, a mathematical model was developed in the form of a queuing system. The results of the numerical experiment make it possible to optimize the time of using local MEC servers to provide video content.

Сервисы онлайн-видео являются одними из самых популярных способов потребления контента. На серверы видео хостинга приходится ежедневно колоссальная нагрузка, которую нами предложено снизить за счёт миграции приложения востребованным видеоконтентом на локальный сервер MEC целевой зоны. Это позволит повысить качество предоставляемых услуг пользователям за счёт сокращения задержки на передачу. Поэтому предложена архитектура, дающая возможность в моменты повышенного спроса на одинаковый видеоконтент производить миграцию приложения видеосервиса на граничные серверы оператора сети. Для оценки показателей эффективности такого подхода была построена математическая модель в виде системы массового обслуживания. Результаты численного эксперимента позволяют произвести оптимизацию времени использования локальных серверов MEC для предоставления видеоконтента.

queuing systemservice migrationMECMarkov processtruncated Markov processvideo contentмиграция сервисовграничные вычисленияMECмарковский процессонлайн-видеоусечённый марковский процессThe reported study was funded by RFBR, project number 20-37-90131 (recipient Dmitry Poluektov).A. Khan, M. Othman, S. A. Madani, and S. U. Khan, “A Survey of Mobile Cloud Computing Application Models,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 393-413, 2014. DOI: 10.1109/SURV.2013.062613.00160.L. Guan, X. Ke, M. Song, and J. Song, “A Survey of Research on Mobile Cloud Computing,” in 2011 10th IEEE/ACIS International Conference on Computer and Information Science, 2011, pp. 387-392. DOI: 10.1109/ICIS.2011.67.H. Dinh Thai, C. Lee, D. Niyato, and P. Wang, “A survey of mobile cloud computing: Architecture, applications, and approaches,” Wireless Communications and Mobile Computing, vol. 13, Dec. 2013. DOI: 10.1002/wcm.1203.X. Fan, J. Cao, and H. Mao, A survey of mobile cloud computing, English, 2011.N. Fernando, S. W. Loke, and W. Rahayu, “Mobile cloud computing: A survey,” Future Generation Computer Systems, vol. 29, no. 1, pp. 84- 106, 2013, Including Special section: AIRCC-NetCoM 2009 and Special section: Clouds and Service-Oriented Architectures. DOI: 10.1016/j.future.2012.05.023.M. Alizadeh, S. Abolfazli, M. Zamani, S. Baharun, and K. Sakurai, “Authentication in mobile cloud computing: A survey,” Journal of Network and Computer Applications, vol. 61, pp. 59-80, 2016. DOI: 10.1016/j.jnca.2015.10.005.A. Ahmed and E. Ahmed, “A survey on mobile edge computing,” in 10th International Conference on Intelligent Systems and Control (ISCO), 2016, pp. 1-8. DOI: 10.1109/ISCO.2016.7727082.M. Beck, M. Werner, S. Feld, and T. Schimper, “Mobile Edge Computing: A Taxonomy,” in The Sixth International Conference on Advances in Future Internet, 2014, pp. 48-54.R. Roman, J. Lopez, and M. Mambo, “Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges,” Future Generation Computer Systems, vol. 78, part 2, pp. 680-698, 2018. DOI: 10.1016/j.future.2016.11.009.S. Yi, C. Li, and Q. Li, “A Survey of Fog Computing: Concepts, Applications and Issues,” in Proceedings of the 2015 Workshop on Mobile Big Data, ser. Mobidata ’15, Hangzhou, China: Association for Computing Machinery, 2015, pp. 37-42. DOI: 10.1145/2757384.2757397.S. Yi, Z. Qin, and Q. Li, “Security and Privacy Issues of Fog Computing: A Survey,” Aug. 2015, pp. 685-695. DOI: 10.1007/978-3-319-218373_67.W. Ali, S. M. Shamsuddin, and A. S. Ismail, “A Survey of Web Caching and Prefetching A Survey of Web Caching and Prefetching,” International Journal of Advances in Soft Computing and its Applications, vol. 3, no. 1, 2011.S. Podlipnig and L. Böszörmenyi, “A Survey of Web Cache Replacement Strategies,” ACM Comput. Surv., vol. 35, no. 4, pp. 374-398, Dec. 2003. DOI: 10.1145/954339.954341.Z. Lv and W. Xiu, “Interaction of Edge-Cloud Computing Based on SDN and NFV for Next Generation IoT,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 5706-5712, 2020. DOI: 10.1109/JIOT.2019.2942719.Z. Ning, P. Dong, X. Wang, X. Hu, L. Guo, B. Hu, Y. Guo, T. Qiu, and R. Y. K. Kwok, “Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 2, pp. 463-478, 2021. DOI: 10.1109/JSAC.2020.3020645.T. Stockhammer, “Dynamic Adaptive Streaming over HTTP: Standards and Design Principles,” in Proceedings of the Second Annual ACM Conference on Multimedia Systems, ser. MMSys ’11, San Jose, CA, USA: Association for Computing Machinery, 2011, pp. 133-144. DOI: 10.1145/1943552.1943572.A. Khakimov, E. Mokrov, D. Poluektov, K. Samouylov, and A. Koucheryavy, “Evaluating the Quality of Experience Performance Metric for UAV-Based Networks,” Sensors, vol. 21, no. 17, 2021. DOI: 10.3390/s21175689.