A Framework for Classification and Visualization of Elephant Flows in SDN-Based Networks

AbstractLong-lived flows termed as elephant flows normally transport large volumes of data in enterprise networks, particularly data center networks. These flows tend to consume a lot of bandwidth and fill up network buffers end-to-end. This causes non-trivial delays for short-lived flows referred to as mice flows which are usually delay-sensitive. Therefore, identifying and handling elephant flows is important for QoS provisioning. In this paper, we present a framework for real-time detection and visualization of elephant flows in SDN-based networks using sFlow. Using our proposed framework, network operators can examine elephant flows through each switch by double-clicking the switch node in the topology visualization UI. Although not in the scope of this paper, but in order to meet traffic engineering requirements, the elephant flows detected and visualized by our proposed framework can be reprioritized, re-scheduled, or routed via dedicated high speed links. We evaluate the proposed framework by using a physical SDN testbed as well as a Mininet-based testbed

Graph Modeling for OpenFlow Switch Monitoring

Network monitoring allows network administrators to facilitate network activities and to resolve issues in a timely fashion. Monitoring techniques in software-defined networks are either (i) active, where probing packets are sent periodically, or (ii) passive, where traffic statistics are collected from the network forwarding elements. The centralized nature of software-defined networking implies the implementation of monitoring techniques imposes additional overhead on the network controller. We propose Graph Modeling for OpenFlow Switch Monitoring (GMSM), which is a lightweight monitoring technique. GMSM constructs a flow-graph overview using two types of asynchronous OpenFlow messages: packet-in and flow-removed, which improve monitoring and decision making. It classifies new flows based on the class of service. Experimental findings suggest that using GMSM leads to a decrease in network overhead resulting from the communication between the controller and the switches, with a reduction of 5.7% and 6.7% compared to state-of-the-art approaches. GMSM reduces the controller’s CPU utilization by more than 2% compared to other monitoring methods. Overhead reduction comes with a slight reduction of approximately 0.17 units in the estimation accuracy of links utilization because GMSM allows the user to monitor the network subject to a selected class of service, as opposed to having an exact view of the network utilization

Pengembangan Aplikasi Pemantauan Jaringan Berbasis Web pada Software-Defined Networking dengan Protokol SFLOW

Software-defined networking (SDN) merupakan salah satu arsitektur jaringan yang dapat diprogram untuk memudahkan manajemen jaringan menggunakan aplikasi pengontrol.. SDN controller seperti ONOS hanya berfungsi untuk manajemen flow table dan tidak memiliki fitur pemantauan traffic jaringan yang cukup untuk mendukung proses manajemen jaringan. Oleh karena itu, untuk melakukan pemantauan traffic SDN maka diperlukan protokol lain seperti sFlow. Pada penelitian ini dikembangkan aplikasi sistem pemantauan traffic SDN berbasis web dengan menggunakan ONOS sebagai SDN controller, sFlow-RT sebagai sFlow collector, dan Node.js sebagai web server. Hasil pengembangan aplikasi menghasilkan tiga buah fitur utama yaitu topologi, grafik traffic, dan laporan. Visualisasi topologi dibuat berdasarkan data topologi dari API ONOS dan ditampilkan menggunakan pustaka vis.js. Kemudian untuk grafik throughput dibuat berdasarkan data traffic dari API sFlow-RT dan ditampilkan menggunakan pustaka dygraph. Data topologi dan traffic yang tertampil pada aplikasi diperbarui setiap 10 detik. Pengujian aplikasi dilakukan dengan black-box testing menunjukkan bahwa semua fungsi pada aplikasi berhasil dilakukan. Hasil survei menunjukkan bahwa aplikasi memiliki tampilan informatif dan ramah pengguna, serta dapat memudahkan pemantauan traffic SDN. AbstractSoftware-defined networking (SDN) is one of the network architectures which programmable to ease network management using the controller application. SDN controllers such as ONOS only function for flow table management and do not have enough network traffic monitoring features to support network management processes. Therefore, to monitor SDN traffic other protocols such as sFlow are needed. In this research, the web-based SDN traffic monitoring system application was developed by using ONOS as SDN controller, sFlow-RT as sFlow collector, and Node.js as a web server. The results of application development produce three main features namely topology, traffic graphs, and reports. The topology visualization is based on topology data from the ONOS API and is displayed using the vis.js library. Then the throughput graph is made based on data traffic from the sFlow-RT API and displayed using the dygraph library. Topology and traffic data displayed on the application are updated every 10 seconds. Application testing is done with black-box testing showing that all functions and features of the application can function properly. The survey conducted shows that the application has an informative and user-friendly display, and can facilitate monitoring of SDN traffic.

Estudos de aplicabilidade de redes neurais para balanceamento de carga em redes de data centers baseados em OpenFlow

Orientador: Christian Rodolfo Esteve RothenbergDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O crescimento dos serviços de aplicativos em nuvem fornecidos por os data centers com demandas de tráfego variáveis revela limitações dos métodos tradicionais de balanceamento de carga. Visando em atender aos cenários em evolução e melhorar o desempenho geral da rede. Esta pesquisa propõe um estudo de balanceamento de carga baseado em uma Rede Neural Artificial (ANN) no contexto da Rede Definido por Conhecimento (KDN). A KDN busca alavancar as técnicas de Inteligência Artificial (AI) para o controle e operação de redes de computadores. O KDN amplia o Redes Definidas por Software (SDN) com telemetria avançada e análise rede, introduzindo o chamado Plano de Conhecimento. A proposta da ANN é capaz de prever o desempenho da rede de acordo com os parâmetros de tráfego, criando um modelo de comportamento de tráfego baseado em medições de largura de banda e latência sobre diferentes caminhos. O estudo inclui o treinamento do modelo ANN para escolher o roteamento de caminho menos carregado. Realizamos uma série de experimentos em um ambiente emulado para validar o estudo proposto. Os resultados experimentais mostram que o desempenho do data center baseado em KDN foi bastante aprimoradoAbstract: The growth of cloud application services delivered through data centers with varying traffic demands unveils the limitations of traditional load balancing study. Aiming at attending the evolving scenarios and improving the overall network performance. This research proposes a load-balancing study based on an Artificial Neural Network (ANN) in the context of Knowledge-Defined Networking (KDN). KDN seeks to leverage Artificial Intelligence (AI) techniques for the control and operation of computer networks. KDN extends Software Defined Networking (SDN) with advanced telemetry and network analytics introducing a so-called Knowledge Plane. The ANN is capable of predicting the network performance according to traffic parameters by creating a model of traffic behavior using the available bandwidth and latency measurements over different paths. The study includes training the ANN model to choose the least loaded path routing. We conduct a series of experiments to verify the proposed study. The experimental results show that the performance of the KDN-based data center has been greatly improvedMestradoEngenharia de ComputaçãoMestre em Engenharia Elétrica134031/2015-6CNP

Pemerataan Beban Jaringan Menggunakan Metode Hybrid (Proactive Dan Reactive) Pada Jaringan Sdn (Software Defined Networking)

Mendistribusikan trafik pada topologi jaringan fat-tree yang sering digunakan di jaringan data center sangatlah penting. Multipath routing adalah teknik umum yang digunakan untuk menyeimbangkan trafik. Dalam Software Defined Networking (SDN), jalur routing sepenuhnya dikendalikan oleh controller untuk memilih jalur optimal atau beberapa jalur untuk meningkatkan throughput trafic-flow dan menurunkan latensi pengiriman data. Beberapa metode telah dilakukan pada penelitian sebelumnya diantaranya Slavica dengan mode controller proactive dan segmentasi traffic-flow dengan mempertimbangkan pemisahan antara TCP dan UDP. Selain itu LABERIO juga telah melakukan pemerataan traffic-flow dengan menggunakan mode controller reactive dan segmentasi traffic-flow dengan mempertimbangkan IP tujuan. Kemudian metode LABERIO disempurnakan dengan metode DLPO dengan tidak melakukan perubahan jalur dan menggantinya dengan merubah nilai priority pada flow-table untuk mengoptimalkan traffic-flow UDP. Akan tetapi ketika terdapat traffic-flow UDP dengan beberapa traffic-flow TCP dengan IP tujuan yang sama, pada metode yang digunakan pada penelitian sebelumnya tidak dapat mengakomodasi keadaan tersebut sehingga menyebabkan traffic-flow tidak merata ke seluruh path yang tersedia. Penelitian ini mengimplementasikan mode hibridisasi controller proactive-reactive pada SDN untuk menyeimbangkan trafik. SDN Controller memonitor utilisasi jalur dan secara reaktif akan menginstal flow-tables ke switch yang sesuai setiap kali ada lonjakan trafik yang signifikan untuk periode tertentu. SDN Controller akan secara proaktif menerapkan flow-table ke switch yang sesuai. Metode Hybrid melakukan segmentasi traffic-flow dengan mempertimbangkan seluruh elemen flow (IP sumber dan tujuan, internet protocol number dan port number sumber dan tujuan). Dengan melakukan beberapa pengujian, diantaranya adalah dengan memberikan beberapa traffic-flow TCP dan UDP secara bersamaan, hasil evaluasi pada topologi fat-treemenunjukkan bahwa bahwa mode hibridisasi yang diusulkan berkinerja lebih baik daripada LABERIO dengan throughput traffic-flow rata – rata sebesar 7,558 Mbps (25,193%) dari Slavica, 2,38 Mbps (7,93%) dari LABERIO dan 3,44 Mbps (11,47%) dari DLPO. Pengujian latensi pengiriman data juga dilakukan dengan memberikan proses copy file dan mendapatkan hasil untuk latensi pengiriman data, metode dapat menurunkan rata – rata sebesar 31 detik dari Slavica, 18 detik dari LABERIO dan 21 detik dari DLPO. Pada kondisi dimana terjadi traffic-spike, metode Hybrid dapat mengakomodasi dengan memberikan time-threshold sebesar 15 detik sehingga tidak melukan perhitungan ulan

Optimising Networks For Ultra-High Definition Video

The increase in real-time ultra-high definition video services is a challenging issue for current network infrastructures. The high bitrate traffic generated by ultra-high definition content reduces the effectiveness of current live video distribution systems. Transcoders and application layer multicasting (ALM) can reduce traffic in a video delivery system, but they are limited due to the static nature of their implementations. To overcome the restrictions of current static video delivery systems, an OpenFlow based migration system is proposed. This system enables an almost seamless migration of a transcoder or ALM node, while delivering real-time ultra-high definition content. Further to this, a novel heuristic algorithm is presented to optimise control of the migration events and destination. The combination of the migration system and heuristic algorithm provides an improved video delivery system, capable of migrating resources during operation with minimal disruption to clients. With the rise in popularity of consumer based live streaming, it is necessary to develop and improve architectures that can support these new types of applications. Current architectures introduce a large delay to video streams, which presents issues for certain applications. In order to overcome this, an improved infrastructure for delivering real-time streams is also presented. The proposed system uses OpenFlow within a content delivery network (CDN) architecture, in order to improve several aspects of current CDNs. Aside from the reduction in stream delay, other improvements include switch level multicasting to reduce duplicate traffic and smart load balancing for server resources. Furthermore, a novel max-flow algorithm is also presented. This algorithm aims to optimise traffic within a system such as the proposed OpenFlow CDN, with the focus on distributing traffic across the network, in order to reduce the probability of blocking

A framework for Traffic Engineering in software-defined networks with advance reservation capabilities

298 p.En esta tesis doctoral se presenta una arquitectura software para facilitar la introducción de técnicas de ingeniería de tráfico en redes definidas por software. La arquitectura ha sido diseñada de forma modular, de manera que soporte múltiples casos de uso, incluyendo su aplicación en redes académicas. Cabe destacar que las redes académicas se caracterizan por proporcionar servicios de alta disponibilidad, por lo que la utilización de técnicas de ingeniería de tráfico es de vital importancia a fin de garantizar la prestación del servicio en los términos acordados. Uno de los servicios típicamente prestados por las redes académicas es el establecimiento de circuitos extremo a extremo con una duración determinada en la que una serie de recursos de red estén garantizados, conocido como ancho de banda bajo demanda, el cual constituye uno de los casos de uso en ingeniería de tráfico más desafiantes. Como consecuencia, y dado que esta tesis doctoral ha sido co-financiada por la red académica GÉANT, la arquitectura incluye soporte para servicios de reserva avanzada. La solución consiste en una gestión de los recursos de red en función del tiempo, la cual mediante el empleo de estructuras de datos y algoritmos específicamente diseñados persigue la mejora de la utilización de los recursos de red a la hora de prestar este tipo de servicios. La solución ha sido validada teniendo en cuenta los requisitos funcionales y de rendimiento planteados por la red GÉANT. Así mismo, cabe destacar que la solución será utilizada en el despliegue piloto del nuevo servicio de ancho de banda bajo demanda de la red GÉANT a finales del 2017

A framework for Traffic Engineering in software-defined networks with advance reservation capabilities

298 p.En esta tesis doctoral se presenta una arquitectura software para facilitar la introducción de técnicas de ingeniería de tráfico en redes definidas por software. La arquitectura ha sido diseñada de forma modular, de manera que soporte múltiples casos de uso, incluyendo su aplicación en redes académicas. Cabe destacar que las redes académicas se caracterizan por proporcionar servicios de alta disponibilidad, por lo que la utilización de técnicas de ingeniería de tráfico es de vital importancia a fin de garantizar la prestación del servicio en los términos acordados. Uno de los servicios típicamente prestados por las redes académicas es el establecimiento de circuitos extremo a extremo con una duración determinada en la que una serie de recursos de red estén garantizados, conocido como ancho de banda bajo demanda, el cual constituye uno de los casos de uso en ingeniería de tráfico más desafiantes. Como consecuencia, y dado que esta tesis doctoral ha sido co-financiada por la red académica GÉANT, la arquitectura incluye soporte para servicios de reserva avanzada. La solución consiste en una gestión de los recursos de red en función del tiempo, la cual mediante el empleo de estructuras de datos y algoritmos específicamente diseñados persigue la mejora de la utilización de los recursos de red a la hora de prestar este tipo de servicios. La solución ha sido validada teniendo en cuenta los requisitos funcionales y de rendimiento planteados por la red GÉANT. Así mismo, cabe destacar que la solución será utilizada en el despliegue piloto del nuevo servicio de ancho de banda bajo demanda de la red GÉANT a finales del 2017