Building a flexible and inexpensive multi-layer switch for software-defined networks

Software-Defined Networking (SDN) is a paradigm which enables the realisation of programmable network through the separation of the control logic from the forwarding functions. This separation is a departure from the traditional architecture. Much of the work done in SDN enabled devices has concentrated on higher end, high speed networks (10s GBit/s 100s GBit/s), rather than the relatively low bandwidth links (10s MBit/s to a few GBit/s) which are seen, for example, in South Africa. As SDN is increasingly becoming more accepted, due to its advantages over the traditional networks, it has been adopted for industrial purposes such as networking in data centres and network providers. The demand for programmable networks is increasing but is limited by the ability of providers to upgrade their infrastructure. In addition, as access to the Internet has become less expensive, the use of Internet is increasing in academic institutions, NGOs, and small to medium enterprises. This thesis details a means of building and managing a small scale Software-Defined Network using commodity hardware and open source tools. Core to the SDN Network illustrated in this thesis is the prototype of a multi-layer SDN switch. The proposed device is targeted to serve lower bandwidth communication (in relation to commercially produced high speed SDN-enabled devices). The performance of the prototype multilayer switch had shown to achieve: data-rates of up to 99.998%, average latencies that are under 40µs during forwarding/switching and under 100µs during routing while using packet sizes between 64 bytes and 1518 bytes, and a jitter of less than 15µs during all tests. This research explores in detail the design, development, and management of a multi-layer switch and its placement and integration in small scale SDN network. This includes testing of Layer 2 forwarding and Layer 3 routing, OpenFlow compliance testing, the management of the switch using created SDN applications, and real life network functionality such as forwarding, routing and VLAN networking to demonstrate its real world applicability

Building a flexible and inexpensive multi-layer switch for software-defined networks

Software-Defined Networking (SDN) is a paradigm which enables the realisation of programmable network through the separation of the control logic from the forwarding functions. This separation is a departure from the traditional architecture. Much of the work done in SDN enabled devices has concentrated on higher end, high speed networks (10s GBit/s 100s GBit/s), rather than the relatively low bandwidth links (10s MBit/s to a few GBit/s) which are seen, for example, in South Africa. As SDN is increasingly becoming more accepted, due to its advantages over the traditional networks, it has been adopted for industrial purposes such as networking in data centres and network providers. The demand for programmable networks is increasing but is limited by the ability of providers to upgrade their infrastructure. In addition, as access to the Internet has become less expensive, the use of Internet is increasing in academic institutions, NGOs, and small to medium enterprises. This thesis details a means of building and managing a small scale Software-Defined Network using commodity hardware and open source tools. Core to the SDN Network illustrated in this thesis is the prototype of a multi-layer SDN switch. The proposed device is targeted to serve lower bandwidth communication (in relation to commercially produced high speed SDN-enabled devices). The performance of the prototype multilayer switch had shown to achieve: data-rates of up to 99.998%, average latencies that are under 40µs during forwarding/switching and under 100µs during routing while using packet sizes between 64 bytes and 1518 bytes, and a jitter of less than 15µs during all tests. This research explores in detail the design, development, and management of a multi-layer switch and its placement and integration in small scale SDN network. This includes testing of Layer 2 forwarding and Layer 3 routing, OpenFlow compliance testing, the management of the switch using created SDN applications, and real life network functionality such as forwarding, routing and VLAN networking to demonstrate its real world applicability

User-Centric Traffic Engineering in Software Defined Networks

Software defined networking (SDN) is a relatively new paradigm that decouples individual network elements from the control logic, offering real-time network programmability, translating high level policy abstractions into low level device configurations. The framework comprises of the data (forwarding) plane incorporating network devices, while the control logic and network services reside in the control and application planes respectively. Operators can optimize the network fabric to yield performance gains for individual applications and services utilizing flow metering and application-awareness, the default traffic management method in SDN. Existing approaches to traffic optimization, however, do not explicitly consider user application trends. Recent SDN traffic engineering designs either offer improvements for typical time-critical applications or focus on devising monitoring solutions aimed at measuring performance metrics of the respective services. The performance caveats of isolated service differentiation on the end users may be substantial considering the growth in Internet and network applications on offer and the resulting diversity in user activities. Application-level flow metering schemes therefore, fall short of fully exploiting the real-time network provisioning capability offered by SDN instead relying on rather static traffic control primitives frequent in legacy networking. For individual users, SDN may lead to substantial improvements if the framework allows operators to allocate resources while accounting for a user-centric mix of applications. This thesis explores the user traffic application trends in different network environments and proposes a novel user traffic profiling framework to aid the SDN control plane (controller) in accurately configuring network elements for a broad spectrum of users without impeding specific application requirements. This thesis starts with a critical review of existing traffic engineering solutions in SDN and highlights recent and ongoing work in network optimization studies. Predominant existing segregated application policy based controls in SDN do not consider the cost of isolated application gains on parallel SDN services and resulting consequence for users having varying application usage. Therefore, attention is given to investigating techniques which may capture the user behaviour for possible integration in SDN traffic controls. To this end, profiling of user application traffic trends is identified as a technique which may offer insight into the inherent diversity in user activities and offer possible incorporation in SDN based traffic engineering. A series of subsequent user traffic profiling studies are carried out in this regard employing network flow statistics collected from residential and enterprise network environments. Utilizing machine learning techniques including the prominent unsupervised k-means cluster analysis, user generated traffic flows are cluster analysed and the derived profiles in each networking environment are benchmarked for stability before integration in SDN control solutions. In parallel, a novel flow-based traffic classifier is designed to yield high accuracy in identifying user application flows and the traffic profiling mechanism is automated. The core functions of the novel user-centric traffic engineering solution are validated by the implementation of traffic profiling based SDN network control applications in residential, data center and campus based SDN environments. A series of simulations highlighting varying traffic conditions and profile based policy controls are designed and evaluated in each network setting using the traffic profiles derived from realistic environments to demonstrate the effectiveness of the traffic management solution. The overall network performance metrics per profile show substantive gains, proportional to operator defined user profile prioritization policies despite high traffic load conditions. The proposed user-centric SDN traffic engineering framework therefore, dynamically provisions data plane resources among different user traffic classes (profiles), capturing user behaviour to define and implement network policy controls, going beyond isolated application management

Big Data Analytics and Application Deployment on Cloud Infrastructure

This dissertation describes a project began in October 2016. It was born from the collaboration between Mr.Alessandro Bandini and me, and has been developed under the supervision of professor Gianluigi Zavattaro. The main objective was to study, and in particular to experiment with, the cloud computing in general and its potentiality in the data elaboration field. Cloud computing is a utility-oriented and Internet-centric way of delivering IT services on demand. The first chapter is a theoretical introduction on cloud computing, analyzing the main aspects, the keywords, and the technologies behind clouds, as well as the reasons for the success of this technology and its problems. After the introduction section, I will briefly describe the three main cloud platforms in the market. During this project we developed a simple Social Network. Consequently in the third chapter I will analyze the social network development, with the initial solution realized through Amazon Web Services and the steps we took to obtain the final version using Google Cloud Platform with its charateristics. To conclude, the last section is specific for the data elaboration and contains a initial theoretical part that describes MapReduce and Hadoop followed by a description of our analysis. We used Google App Engine to execute these elaborations on a large dataset. I will explain the basic idea, the code and the problems encountered

Communication patterns abstractions for programming SDN to optimize high-performance computing applications

Orientador : Luis Carlos Erpen de BonaCoorientadores : Magnos Martinello; Marcos Didonet Del FabroTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 04/09/2017Inclui referências : f. 95-113Resumo: A evolução da computação e das redes permitiu que múltiplos computadores fossem interconectados, agregando seus poderes de processamento para formar uma computação de alto desempenho (HPC). As aplicações que são executadas nesses ambientes processam enormes quantidades de informação, podendo levar várias horas ou até dias para completar suas execuções, motivando pesquisadores de varias áreas computacionais a estudar diferentes maneiras para acelerá-las. Durante o processamento, essas aplicações trocam grandes quantidades de dados entre os computadores, fazendo que a rede se torne um gargalo. A rede era considerada um recurso estático, não permitindo modificações dinâmicas para otimizar seus links ou dispositivos. Porém, as redes definidas por software (SDN) emergiram como um novo paradigma, permitindoa ser reprogramada de acordo com os requisitos dos usuários. SDN já foi usado para otimizar a rede para aplicações HPC específicas mas nenhum trabalho tira proveito dos padrões de comunicação expressos por elas. Então, o principal objetivo desta tese é pesquisar como esses padrões podem ser usados para ajustar a rede, criando novas abstrações para programá-la, visando acelerar as aplicações HPC. Para atingir esse objetivo, nós primeiramente pesquisamos todos os níveis de programabilidade do SDN. Este estudo resultou na nossa primeira contribuição, a criação de uma taxonomia para agrupar as abstrações de alto nível oferecidas pelas linguagens de programação SDN. Em seguida, nós investigamos os padrões de comunicação das aplicações HPC, observando seus comportamentos espaciais e temporais através da análise de suas matrizes de tráfego (TMs). Concluímos que as TMs podem representar as comunicações, além disso, percebemos que as aplicações tendem a transmitir as mesmas quantidades de dados entre os mesmos nós computacionais. A segunda contribuição desta tese é o desenvolvimento de um framework que permite evitar os fatores da rede que podem degradar o desempenho das aplicações, tais como, sobrecarga imposta pela topologia, o desbalanceamento na utilização dos links e problemas introduzidos pela programabilidade do SDN. O framework disponibiliza uma API e mantém uma base de dados de TMs, uma para cada padrão de comunicação, anotadas com restrições de largura de banda e latência. Essas informações são usadas para reprogramar os dispositivos da rede, alocando uniformemente as comunicações nos caminhos da rede. Essa abordagem reduziu o tempo de execução de benchmarks e aplicações reais em até 26.5%. Para evitar que o código da aplicação fosse modificado, como terceira contribuição, desenvolvemos um método para identificar automaticamente os padrões de comunicação. Esse método gera texturas visuais di_erentes para cada TM e, através de técnicas de aprendizagem de máquina (ML), identifica as aplicações que estão usando a rede. Em nossos experimentos, o método conseguiu uma taxa de acerto superior a 98%. Finalmente, nós incorporamos esse método ao framework, criando uma abstração que permite programar a rede sem a necessidade de alterar as aplicações HPC, diminuindo em média 15.8% seus tempos de execução. Palavras-chave: Redes Definidas por Software, Padrões de Comunicação, Aplicações HPC.Abstract: The evolution of computing and networking allowed multiple computers to be interconnected, aggregating their processing powers to form a high-performance computing (HPC). Applications that run in these computational environments process huge amounts of information, taking several hours or even days to complete their executions, motivating researchers from various computational fields to study different ways for accelerating them. During the processing, these applications exchange large amounts of data among the computers, causing the network to become a bottleneck. The network was considered a static resource, not allowing dynamic adjustments for optimizing its links or devices. However, Software-Defined Networking (SDN) emerged as a new paradigm, allowing the network to be reprogrammed according to users' requirements. SDN has already been used to optimize the network for specific HPC applications, but no existing work takes advantage of the communication patterns expressed by those applications. So, the main objective of this thesis is to research how these patterns can be used for tuning the network, creating new abstractions for programming it, aiming to speed up HPC applications. To achieve this goal, we first surveyed all SDN programmability levels. This study resulted in our first contribution, the creation of a taxonomy for grouping the high-level abstractions offered by SDN programming languages. Next, we investigated the communication patterns of HPC applications, observing their spatial and temporal behaviors by analyzing their traffic matrices (TMs). We conclude that TMs can represent the communications, furthermore, we realize that the applications tend to transmit the same amount of data among the same computational nodes. The second contribution of this thesis is the development of a framework for avoiding the network factors that can degrade the performance of applications, such as topology overhead, unbalanced links, and issues introduced by the SDN programmability. The framework provides an API and maintains a database of TMs, one for each communication pattern, annotated with bandwidth and latency constraints. This information is used to reprogram network devices, evenly placing the communications on the network paths. This approach reduced the execution time of benchmarks and real applications up to 26.5%. To prevent the application's source code to be modified, as a third contribution of our work, we developed a method to automatically identify the communication patterns. This method generates different visual textures for each TM and, through machine learning (ML) techniques, identifies the applications using the network. In our experiments the method succeeded with an accuracy rate over 98%. Finally, we incorporate this method into the framework, creating an abstraction that allows programming the network without changing the HPC applications, reducing on average 15.8% their execution times. Keywords: Software-Defined Networking, Communication Patterns, HPC Applications