Transmissão por múltiplos caminhos com perda de pacotes em MPTCP

Orientador: Eduardo Parente RibeiroDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 27/07/2018Inclui referências: p. 61-64Área de concentração: TelecomunicaçõesResumo: A heterogeneidade dos caminhos disponíveis nos dispositivos atuais, como redes WiFi, WiMAX e LTE, causa grande impacto no atraso e na taxa de transferência quando são empregados protocolos de múltiplos caminhos, como o Multipath TCP (MPTCP). Um dos principais problemas causadores dessa situação é o atraso decorrente do reordenamento dos pacotes no buffer de recebimento. Este trabalho propõe uma análise do impacto na taxa de transferência de uma conexão MPTCP de um caminho degradado por perda de pacotes, estuda um limiar da razão de perda de pacotes a partir do qual é melhor utilizar uma única conexão TCP pelo melhor caminho disponível e, por fim, propõe um método dinâmico para a filtragem do caminho mais degradado. Este trabalho descreve testes em cenários de rede heterogêneas comumente encontrados em dispositivos móveis, compostos por uma conexão WiFi e outra de rede celular, no simulador de redes NS-3. Palavras-chave: MPTCP, escalonador, multi-abrigado, multi-caminhos, seleção de caminho.Abstract: The heterogeneity of available paths in the current devices, such as WiFi, WiMAX and LTE networks, has a great impact on delay and throughput when multipath protocols, such as Multipath TCP, are used. One of the main causes of this situation is the delay caused by reordering the packets in the receive buffer. This work proposes an analysis of the impact on the transfer rate of an MPTCP connection of a degraded path by the packet loss. A threshold of packet loss ratio from which it is best to use a single TCP connection with the best available path is studied, and, finaly, a dynamic method for filtering the most degraded path is proposed. This work describes tests in a heterogeneous network scenario commonly seen in mobile devices, consisting of aWiFi connection and a cellular network, in the NS-3 network simulator. Keywords: MPTCP, scheduler, multihomed, multipath, path selection

Leveraging Machine Learning Techniques towards Intelligent Networking Automation

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the computational costs of implementing the proposed mechanisms. Accordingly, this thesis tackles the challenges that four specific research problems present. The first topic addresses the problem of balancing traffic in dense Internet of Things (IoT) network scenarios where the end devices and the Base Stations (BSs) form complex networks. By applying ML techniques to discover patterns in the association between the end devices and the BSs, the proposed scheme can balance the traffic load in a IoT network to increase the packet delivery ratio and reduce the energy cost of data delivery. The second research topic proposes an intelligent congestion control for internet connections at edge network elements. The design includes a congestion predictor based on an Artificial Neural Network (ANN) and an Active Queue Management (AQM) parameter tuner. Similarly, the third research topic includes an intelligent solution to the inter-domain congestion. Different from second topic, this problem considers the preservation of the private network data by means of Federated Learning (FL), since network elements of several organizations participate in the intelligent process. Finally, the fourth research topic refers to a framework to efficiently gathering network telemetry (NT) data. The proposed solution considers a traffic-aware approach so that the NT is intelligently collected and transmitted by the network elements. All the proposed schemes are evaluated through use cases considering standardized networking mechanisms. Therefore, we envision that the solutions of these specific problems encompass a set of methods that can be utilized in real-world scenarios towards the realization of the INA paradigm