Zaawansowane metody optymalizacji wielowarstwowych sieci uwzględniających aplikacje
Kierownik prof. dr hab. inż. Krzysztof Walkowiak Kierownik K. Walkowiak

Opis projektu

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.

The network optimization can be performed considering each single layer of the network separately. However, multilayer optimization allows to improve the overall network performance, namely, reduce CAPEX expenditures and at the same time holding the QoS (Quality of Service) parameters required by the applications by identifying the network configurations that jointly optimizes packet and optical resources. The idea of application-aware networking has been known for many years, however the vast majority of previous works in this area have been focused on a single layer (packet layer) optimization, i.e., the routing algorithms have been aware only of the packet layer resources and constraints. While at the same time, the configuration of optical layer resources have not been updated according to changing requirements. Moreover, recent trends in application characteristics, namely, the increasing diversity of requirements in almost all performance indicators (e.g., bitrate, latency, resilience, security) and new network capabilities trigger the need of multilayer application-aware networking.

In this Project, we form a hypothesis that it is possible to design new optimization methods for multilayer application-aware networks in order to improve the network performance by utilizing additional information that can be provided by cognitive processes including data analytics mechanisms based on machine learning methods. Potential recipients of the Project results are network operators, network service providers, and producers of network equipment and software, since results of the Project can be applied to design and optimize communication networks.

News and Seminars

2022.08.02 Paper (Knapińska A., Lechowicz P., Walkowiak K., “Prediction of Multiple Types of Traffic with a Novel Evaluation Metric Related to Bandwidth Blocking“) accepted to the IEEE Global Communications Conference GLOBECOM 2022 (link held December 4-8, 2022 in Rio de Janeiro, Brazil.

2022.07.13 Paper (Lechowicz P., Knapińska A., Walkowiak K., “Delayed Squeezed Dedicated Path Protection in Spectrally-Spatially Flexible Optical Networks“) accepted to the 12th International Workshop on Resilient Networks Design and Modeling RNDM 2022 (link held September 19-21, 2022 in Compiegne, France.

2022.05.17 Seminar: Prof. Krzysztof Walkowiak “Intent-based networking”

2022.03.29 Paper (Knapińska A., Półtorak K., Poręba D., Miszczyk J., Daniluk M., Walkowiak K., “On Feature Selection in Short-Term Prediction of Backbone Optical Network Traffic“) accepted to the 26th International Conference on Optical Network Design and Modelling ONDM 2022 (link held May 16-19, 2022 in Warsaw, Poland.

2022.03.29 Paper (Goścień R., Knapińska A., “Efficient Network Traffic Prediction After a Node Failure“ [INVITED]) accepted to the 26th International Conference on Optical Network Design and Modelling ONDM 2022 (link held May 16-19, 2022 in Warsaw, Poland.

2022.03.25 Paper (Cembaluk P., Aniszewski J., Knapińska A., Walkowiak K., “Forecasting the Network Traffic with PROPHET“) accepted to the 3rd Polish Conference on Artificial Intelligence PP-RAI’2022 (link held April 25-27, 2022 in Gdynia, Poland.

2022.01.25 Seminar: Aleksandra Knapińska “Long-term prediction of multiple types of time-varying network traffic using chunk-based ensemble learning”

2021.12.07 Seminar: Dr Piotr Lechowicz "Optimization of transceiver allocation for time-varying traffic"

2021.12.07 Seminar: Daniel Szostak "Timeseries analysis for network traffic forecasting”

2021.07.07 Paper (Goścień R., Knapińska A., Włodarczyk A., “Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study“) accepted to Electronics 2021, 10(14), DOI information:

2021.06.30 Seminar: Aleksandra Knapińska “Long-term traffic prediction with ensemble learning on data streams”

2021.06.30 Seminar: Dr Róża Goścień “On the Efficient Flow Restoration in Spectrally-Spatially Flexible Optical Networks”

2021.06.12 Paper (Lechowicz P., Knapińska A., Goścień R., “Fragmentation-Aware Traffic Grooming with Lane Changes in Spectrally–Spatially Flexible Optical Networks“) accepted to Electronics 2021, 10(12), DOI information:

2021.06.11 Paper (Goścień R., “On the Efficient Flow Restoration in Spectrally-Spatially Flexible Optical Networks“) accepted to Electronics 2021, 10(12), DOI information:

2021.03.31 Seminar: Aleksandra Knapińska “Multilayer Application-Aware Networks - Literature Review”

2021.03.31 Seminar: Daniel Szostak “Survey of Machine Learning approaches for Network Traffic Forecasting”

2021.03.17 Paper (Knapińska A., Lechowicz P., Walkowiak K., “Machine-Learning Based Prediction of Multiple Types of Network Traffic“) accepted to the International Conference on Computational Science ICCS 2021 (link held June 16-18, 2021 in Kraków, Poland.

2021.02.03 Seminar: Aleksandra Knapińska “Machine-Learning Based Prediction of Multiple Types of Network Traffic”

2021.02.03 Seminar: Damian Mroziński “Optimization of conergent packet switched 5G transport networks with latency-sensitive traffic flows”

2021.02.03 Seminar: Dominik Dulas “AI-assisted dimensioning methods for Network Slicing in Next Generation Mobile Networks”

2020.12.02 Seminar: Dr Piotr Lechowicz “Regression-based fragmentation metrics in spectrally-spatially flexible optical networks”

2020.12.02 Seminar: Prof. Miroslaw Klinkowski (National Institute of Telecommunications, Warsaw) “Optimization of 5G conergent transport networks with packet switching and time sensitive traffic”

2020.10.07 Seminar: Prof. Krzysztof Walkowiak “Advanced methods for optimization of multilayer application-aware networks – Project kick-off meeting”


AT&T testing cloud gaming traffic prioritization

Cloud gaming is an example of network traffic that not only requires fast download speeds but, more importantly, low latency. #maan

Telefonica and Ericsson demonstrate end-to-end automated network slicing

Telefonica and Ericsson recently provided a demonstration of end-to-end automated network slicing #maan

The US Army to utilize network slicing

As the Army’s current networks consist of many stove-piped transport networks that were developed for specific purposes, the investment in network slicing will vastly improve the connectivity. #maan

Deutsche Telekom and Ericsson demonstrate network slicing

Deutsche Telekom and Ericsson announced a proof of concept implementation of global 5G end-to-end network slicing for latency-critical enterprise applications with guaranteed Quality of Service #maan

Increasing app complexity restricts QoS ensurance

The addition of embedded video, chat, payment, and other features to multiple applications increases their QoS requirements and makes them difficult to identify by conventional techniques. #maan

The surge of heavy network users

According to the recent Sandvine Global Internet Phenomena Report, there is an increase in 1TB per Month of power users.  #maan