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Currently, we observe a high popularity of the traffic-aware network management and optimization approaches, which benefit from the traffic modeling and prediction tools. The efficiency of these approaches depends on the accuracy of the applied modeling and prediction methods, which might be significantly decreased by exceptional events and anomali...
The knowledge about future traffic volumes is beneficial for the network operators in many areas. Short-term forecasting of multiple traffic types helps with efficient resource utilization by enabling near real-time adjustment. An important issue is the choice of a suitable prediction model to obtain the most accurate traffic forecasts. A machine l...
With the constant development of networking technologies and the increase of internet userbase, traffic prediction is becoming a vital part of today’s network optimization. In this paper, we propose a method for network traffic prediction based on the PROPHET model. We examine its different parameters find their best configuration for diverse traff...
The paper studies efficient modeling and prediction of daily traffic patterns in transport telecommunication networks. The investigation is carried out using two historical datasets, namely WASK and SIX, which collect flows from edge nodes of two networks of different size. WASK is a novel dataset introduced and analyzed for the first time in this...
Traffic in current networks is constantly increasing due to the growing popularity of various network services. The currently deployed backbone optical networks apply wavelength division multiplexing (WDM) techniques in single-core single-mode fibers (SMFs) to transmit the light. However, the capacity of SMFs is limited due to physical constraints,...
Prior knowledge regarding approximated future traffic requirements allows adjusting suitable network parameters to improve the network’s performance. To this end, various analyses and traffic prediction methods assisted with machine learning techniques are developed. In this paper, we study on-line multiple time series prediction for traffic of var...
This project is focused on optimization of multilayer application-aware networks. The key goal of the project is to develop, implement, and analyze models and algorithms for optimization of multilayer application-aware networks. An application-aware network can be defined as a network that is able to identify and classify applications and then use suitable optimization techniques to provision these application using resources accessible in the network in order to achieve acceptable application performance metrics. In turn, a multilayer network is a network modeled as a set of separate layers using various technologies and protocols applied to transmit data. In the context of this project, we assume that the network consists of two layers: packet layer and optical layer. The packet layer is used to directly serve the applications, i.e., to establish in the network demands required to serve various types of applications. In turn, the optical layer is used to establish virtual topologies to provision flows aggregated over the packet layer service demands. Project home page: https://www.kssk.pwr.edu.pl/projects/maan