tuzsutТруды учебных заведений связиProceedings of Telecommunication Universities1813-324X2712-8830СПбГУТ10.31854/1813-324X-2021-7-1-132-140tuzsut-159Research ArticleТРУДЫ МОЛОДЫХ УЧЕНЫХYOUNG SCHOLARS RESEARCHПрограммная методика оценки эффективности аппаратного состава серверов системы глубокой инспекции пакетов с использованием модернизированного метода Хука ‒ ДживсаSoftware Methodology for Estimating the Efficiency of the Hardware Composition of Deep Packet Inspection System Using the Modernized Hooke ‒ Jeeves Methodhttps://orcid.org/0000-0002-3226-927XФицовВ. В.FitsovV.Санкт-Петербургский государственный университет телекоммуникаций им. проф. М.А. Бонч-БруевичаРоссияThe Bonch-Bruevich Saint-Petersburg State University of TelecommunicationsRussian Federation20211804202171132140Copyright © Фицов В.В., 20212021Фицов В.В.Fitsov V.This work is licensed under a Creative Commons Attribution 4.0 License.https://tuzs.sut.ru/jour/article/view/159

 Системы глубокой инспекции пакетов на сетях связи используются для распознавания приложения порождающего конкретный поток трафика. Вопросы, связанные с моделированием и проектированием систем глубокой инспекции пакетов, остаются малоизученными. В данной работе приводится программная методика оценки эффективности аппаратного состава серверов системы глубокой инспекции пакетов, использующая математическую модель такой системы и методы программного поиска. Дается описание программного поиска методом максимального элемента и методом Хука ‒ Дживса. Предложена модернизация метода Хука ‒ Дживса для монотонно убывающей функции. Проведено сравнение методов по числу шагов поиска. 

 Deep packet inspection systems on communication networks are used to identify the application generating a specific traffic flow. The issues related to modeling and design of deep packet inspection systems remain poorly understood. In this paper, a software technique for evaluating the effectiveness of the hardware composition of the servers of the deep packet inspection system is presented, using a mathematical model of such a system and software search methods. The description of the program search by the maximum element method and the Hook-Jeeves method is given. A modernization of the Hook-Jeeves method for a monotonically decreasing function is proposed. Comparison of the methods by the number of search steps is performed. 

глубокая инспекция пакетовпрограммный поискметод максимального элементаметод Хука ‒ Дживсаматематическая модельprogrammatic searchmaximum element methodHooke-Jeeves methodmathematical modelReferencesСенченко Ю.Л. Некоторые аспекты высокоскоростной обработки трафика // Технологии и средства связи. 2013. № 1(94). С. 52−53.Senchenko Yu.L. Some Aspects of High-Speed Traffic Processing. Tekhnologii i sredstva sviazi. 2013;1(94):52−53. (in Russ.)Trammell B., Boschi E., Procissi G., Callegari C., Dorfinger P., Schatzmann D. Identifying Skype Traffic in a Large-Scale Flow Data Repository // Proceedings of the 3rd International Workshop on Traffic Monitoring and Analysis (TMA, Vienna, Austria, 27 April 2011). Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2011. Vol. 6613. PP. 72−85. DOI:10.1007/9783-642-20305-3_7Trammell B., Boschi E., Procissi G., Callegari C., Dorfinger P., Schatzmann D. Identifying Skype Traffic in a Large-Scale Flow Data Repository. Proceedings of the 3rd International Workshop on Traffic Monitoring and Analysis, TMA, 27 April 2011, Vienna, Austria. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2011. vol.6613. p.72−85. DOI:10.1007/978-3-64220305-3_7Sommer R., Feldmann A. NetFlow: Information loss or win? // Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment (IMW '02, Marseille, France, November, 2002). New York: Association for Computing Machinery, 2002. PP. 173‒174. DOI: 10.1145/637201.637226Sommer R., Feldmann A. NetFlow: Information loss or win? Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment, IMW '02, November, 2002, Marseille, France. New York: Association for Computing Machinery; 2002. p.173‒174. DOI: 10.1145/637201.637226Park J., Yoon S., Kim M. Software Architecture for a Lightweight Payload Signature-Based Traffic Classification System // Proceedings of the 3rd International Workshop on Traffic Monitoring and Analysis (TMA, Vienna, Austria, 27 April 2011). Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2011. Vol. 6613. PP. 136−149. DOI:10.1007/978-3-64220305-3_12Park J., Yoon S., Kim M. Software Architecture for a Lightweight Payload Signature-Based Traffic Classification System. Proceedings of the 3rd International Workshop on Traffic Monitoring and Analysis, TMA, 27 April 2011, Vienna, Austria. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2011. vol.6613. p.136−149. DOI:10.1007/978-3-642-20305-3_12Deart V., Mankov V., Krasnova I. Agglomerative Clustering of Network Traffic Based on Various Approaches to Determining the Distance Matrix // Proceedings of the 28th Conference of Open Innovations Association (FRUCT’28, Moscow, Russia, 27‒29 January 2021). IEEE, 2021. Vol. 1. PP. 81−88. DOI:10.23919/FRUCT50888.2021.9347616Deart V., Mankov V., Krasnova I. Agglomerative Clustering of Network Traffic Based on Various Approaches to Determining the Distance Matrix. Proceedings of the 28th Conference of Open Innovations Association, FRUCT’28, 27‒29 January 2021, Moscow, Russia. IEEE; 2021. vol.1. p.81−88. DOI:10.23919/FRUCT50888.2021.9347616Dainotti A., Pescape A., Sansone C. Early Classification of Network Traffic through Multi-classification // Proceedings of the 3rd International Workshop on Traffic Monitoring and Analysis (TMA, Vienna, Austria, 27 April 2011). Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2011. Vol. 6613. PP. 122−135. DOI:10.1007/978-3-642-20305-3_11Dainotti A., Pescape A., Sansone C. Early Classification of Network Traffic through Multi-classification. Proceedings of the 3rd International Workshop on Traffic Monitoring and Analysis, TMA, 27 April 2011, Vienna, Austria. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2011. vol.6613. p.122−135. DOI:10.1007/978-3-642-20305-3_11Sheluhin O., Kazhemskiy M. Influence Of Fractal Dimension Statistical Charachteristics On Quality Of Network Attacks Binary Classification // Proceedings of the 28th Conference of Open Innovations Association (FRUCT’28, Moscow, Russia, 27‒ 29 January 2021). IEEE, 2021. Vol. 1. 2021. PP. 407−413. DOI:10.23919/FRUCT50888.2021.9347600Sheluhin O., Kazhemskiy M. Influence Of Fractal Dimension Statistical Charachteristics On Quality Of Network Attacks Binary Classification. Proceedings of the 28th Conference of Open Innovations Association, FRUCT’28, 27‒29 January 2021, Moscow, Russia. IEEE; 2021. vol.1. p.407−413. DOI:10.23919/FRUCT50888.2021.9347600Cascarano N., Ciminiera L., Risso F. Optimizing Deep Packet Inspection for High-Speed Traffic Analysis // Journal of Network and Systems Management. 2011. Vol. 19. PP. 7−31. DOI:10.1007/s10922-010-9181-xCascarano N., Ciminiera L., Risso F. Optimizing Deep Packet Inspection for High-Speed Traffic Analysis. Journal of Network and Systems Management. 2011;19:7−31. DOI:10.1007/s10922-010-9181-xNiang B. Bandwidth management ‒ A deep packet inspection mathematical model // Proceedings of the 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT, St. Petersburg, Russia, 6-8 October 2014). IEEE, 2014. PP. 169−175. DOI:10.1109/ICUMT.2014.7002098Niang B. Bandwidth management ‒ A deep packet inspection mathematical model. Proceedings of the 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT, St. Petersburg, Russia, 6-8 October 2014. IEEE; 2014. p.169−175. DOI:10.1109/ICUMT.2014.7002098Goldstein B., Fitsov V. Dual Mathematical Model for Calculating of Deep Packet Inspection // Proceedings of the 28th Conference of Open Innovations Association (FRUCT’28, Moscow, Russia, 27‒29 January 2021). IEEE, 2021. Vol. 1. 2021. PP. 127−133. DOI:10.23919/FRUCT50888.2021.9347547Goldstein B., Fitsov V. Dual Mathematical Model for Calculating of Deep Packet Inspection. Proceedings of the 28th Conference of Open Innovations Association, FRUCT’28, 27‒29 January 2021, Moscow, Russia. IEEE; 2021. vol.1. p.127−133. DOI:10.23919/FRUCT50888.2021.9347547Фицов В. Методы построения сетевых архитектур систем DPI // Вестник связи. 2020. № 12. С. 32−37.Fitsov V. Methods for Constructing Network Architectures of Dpi Systems. Vestnik Svyazi. 2020;12:32−37. (in Russ.)Фицов В. Применение программного кода для оптимизации числа серверов DPI методом максимального элемента // VII Международная научно-техническая и научно-методическая конференция «Актуальные проблемы инфотелекоммуникаций в науке и образовании» (Санкт-Петербург, Россия, 28 февраля – 01 марта 2018). СПб: СПбГУТ, 2018. С. 650−656.Fitsov V. Employment Software Code for Optimization the Number of Dpi-Servers by Maximal Element Method. Proceedings of the VIIIth International Conference on Infotelecommunications in Science and Education, 28 February ‒ 1 March 2018, St. Petersburg, Russia. St. Petersburg: The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Publ.; 2018. p.650−656. (in Russ.)Якимович С. Управление трафиком и услугами в сетях ШПД с помощью решений DPI // Вестник связи. 2010. № 12. С. 27−29.Yakimovich, S. Controlling Traffic and Services in Broadband Networks Using DPI Solutions. Vestnik Svyazi. 2010;12: 27−29. (in Russ.)Rec. ITU-T Y.2771 Framework for deep packet inspection. ITU, 2014.Rec. ITU-T Y.2771 Framework for deep packet inspection. ITU; 2014.Norros I. A storage model with self-similar input // Queueing Systems. 1994. Iss. 16. PP. 387−396. DOI:10.1007/ BF01158964Norros I. A storage model with self-similar input. Queueing Systems. 1994;16:387−396. DOI:10.1007/ BF01158964Овчаров Л. Прикладные задачи теории массового обслуживания. М.: Машиностроение, 1969. 324 с.Ovcharov L. Applied Problems of Queuing Theory. Moscow: Mashinostroenie Publ; 1969. 324 p. (in Russ.)Hooke R., Jeeves T.A. «Direct Search» Solution of Numerical and Statistical Problems // Journal of the ACM. 1961. Vol. 8. Iss. 2. PP. 212−229. DOI:10.1145/321062.321069Hooke R., Jeeves T.A. «Direct Search» Solution of Numerical and Statistical Problems. Journal of the ACM. 1961;8(2): 212−229. DOI:10.1145/321062.321069Сулимов В., Шкапов П., Носачев С. Локальный поиск методом Хука ‒ Дживса в гибридном алгоритме глобальной оптимизации // Наука и образование. 2014. № 6. С. 107−123. DOI:10.7463/0614.0716155Sulimov V.D., Shkapov P.M., Nosachev S.K. Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm. Science and Education. 2014;6:107−123. DOI:10.7463/0614.0716155 (in Russ.)

The authors declare that there are no conflicts of interest present.