Прогнозирование сетевого трафика при помощи авторегрессионных моделей

Овчинников, К. А.; Бушманов, В. С.

Публікація:
Прогнозирование сетевого трафика при помощи авторегрессионных моделей

dc.contributor.author	Овчинников, К. А.
dc.contributor.author	Бушманов, В. С.
dc.date.accessioned	2016-09-21T07:22:37Z
dc.date.available	2016-09-21T07:22:37Z
dc.date.issued	2013
dc.description.abstract	The predictability of network traffic plays a significant role in many domains such as congestion control, admission control, and network management. An accurate traffic prediction model should have the ability to capture main traffic characteristics, such as long - and short - range dependences, self - similarity etc. In this paper two models of network traffic were analyzed and it is shown that time series simple autoregressive model (AR) provides good performance on short - term predictions. It is also shown that non - stationary time series representing number of packet on router’s port can be des cribed using first - order difference ARIMA model. It was also discovered that increasing number of lag variables of AR network traffic models leads to reduction of its performance. Suggested models can be used as a part of automatic management system for prediction - based routing problem solution.	uk_UA
dc.identifier.citation	Овчинников К.А., Бушманов В.С. Прогнозирование сетевого трафика при помощи авторегрессионных моделей // Проблемы инфокоммуникаций. Наука и технологии (PIC S&T-2013): Сборник научных трудов первой международной научно-практической конференции, Харьков 9-11 октября 2013 г. / М-во образования и науки Украины, Харьковский национальный университет радиоэлектроники. –Харьков: ХНУРЭ, 2013. –С.177-179.	uk_UA
dc.identifier.uri	http://openarchive.nure.ua/handle/document/2957
dc.language.iso	ru	uk_UA
dc.publisher	ХНУРЭ	uk_UA
dc.title	Прогнозирование сетевого трафика при помощи авторегрессионных моделей	uk_UA
dc.type	Conference proceedings	uk_UA
dspace.entity.type	Publication