Project

NCN OPUS 18 "Advanced methods for optimization of multilayer application-aware networks"

Goal: 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

Date: 1 October 2020 - 30 September 2024

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Project log

Aleksandra Knapińska
added 2 research items
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 anomalies, like for instance a long-lasting node failure. After such cases, the modeling and prediction tools may provide low-accuracy and misleading data, which used as an input to management/optimization methods might significantly decrease the network performance. Therefore, it is crucial to evaluate the approaches after such events, draw conclusions regarding their reliability and define application instructions for some special cases. The presented study answers that problem and evaluates how much we can rely on the traffic modeling and prediction approaches when a node failure occurs in a network. It compares a number of approaches and tries to select the most reliable one. The main comparison criterion relates to the time necessary to detect a change in the traffic pattern, adapt models to that event and restore a system convergence.
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 learning (ML) algorithm picked for this task can be further tuned by an appropriate feature selection. In this paper, we propose three models containing sets of additional input features to improve the prediction quality of different ML algorithms. We evaluate our models on multiple datasets containing diverse types of network traffic. In extensive numerical experiments, we prove the high prediction quality of ML regression algorithms aided by our proposed additional features. Obtained mean absolute percentage errors (MAPE) are, depending on the predicted traffic type, as little as 1–10%.
Aleksandra Knapińska
added a research item
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 traffic types. Our research has shown that PROPHET is an accurate solution for backbone optical network traffic forecasting for a 14-day horizon.
Aleksandra Knapińska
added a research item
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 various frame sizes. Firstly, we describe the gathered real network traffic data and study their seasonality and correlations between traffic types. Secondly, we propose three machine learning algorithms, namely, linear regression, k nearest neighbours, and random forest, to predict the network data which are compared under various models and input features. To evaluate the prediction quality, we use the root mean squared percentage error (RMSPE). We define three machine learning models, where traffic related to particular frame sizes is predicted based on the historical data of corresponding frame sizes solely, several frame sizes, and all frame sizes. According to the performed numerical experiments on four different datasets, linear regression yields the highest accuracy when compared to the other two algorithms. As the results indicate, the inclusion of historical data regarding all frame sizes to predict summary traffic of a certain frame size increases the algorithm’s accuracy at the cost of longer execution times. However, by appropriate input features selection based on seasonality, it is possible to decrease this time overhead at the almost unnoticeable accuracy decrease.
Krzysztof Walkowiak
added a project goal
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.