The Role of Inter-Controller Traffic for Placement of Distributed SDN Controllers

We consider a distributed Software Defined Networking (SDN) architecture adopting a cluster of multiple controllers to improve network performance and reliability. Besides the Openflow control traffic exchanged between controllers and switches, we focus on the control traffic exchanged among the controllers in the cluster, needed to run coordination and consensus algorithms to keep the controllers synchronized. We estimate the effect of the inter-controller communications on the reaction time perceived by the switches depending on the data-ownership model adopted in the cluster. The model is accurately validated in an operational Software Defined WAN (SDWAN). We advocate a careful placement of the controllers, that should take into account both the above kinds of control traffic. We evaluate, for some real ISP network topologies, the delay tradeoffs for the controllers placement problem and we propose a novel evolutionary algorithm to find the corresponding Pareto frontier. Our work provides novel quantitative tools to optimize the planning and the design of the network supporting the control plane of SDN networks, especially when the network is very large and in-band control plane is adopted. We also show that for operational distributed controllers (e.g. OpenDaylight and ONOS), the location of the controller which acts as a leader in the consensus algorithm has a strong impact on the reactivity perceived by switches.Comment: 14 page

ICONA: Inter Cluster ONOS Network Application

Several Network Operating Systems (NOS) have been proposed in the last few years for Software Defined Networks; however, a few of them are currently offering the resiliency, scalability and high availability required for production environments. Open Networking Operating System (ONOS) is an open source NOS, designed to be reliable and to scale up to thousands of managed devices. It supports multiple concurrent instances (a cluster of controllers) with distributed data stores. A tight requirement of ONOS is that all instances must be close enough to have negligible communication delays, which means they are typically installed within a single datacenter or a LAN network. However in certain wide area network scenarios, this constraint may limit the speed of responsiveness of the controller toward network events like failures or congested links, an important requirement from the point of view of a Service Provider. This paper presents ICONA, a tool developed on top of ONOS and designed in order to extend ONOS capability in network scenarios where there are stringent requirements in term of control plane responsiveness. In particular the paper describes the architecture behind ICONA and provides some initial evaluation obtained on a preliminary version of the tool.Comment: Paper submitted to a conferenc

Control plane optimization in Software Defined Networking and task allocation for Fog Computing

As the next generation of mobile wireless standard, the fifth generation (5G) of cellular/wireless network has drawn worldwide attention during the past few years. Due to its promise of higher performance over the legacy 4G network, an increasing number of IT companies and institutes have started to form partnerships and create 5G products. Emerging techniques such as Software Defined Networking and Mobile Edge Computing are also envisioned as key enabling technologies to augment 5G competence. However, as popular and promising as it is, 5G technology still faces several intrinsic challenges such as (i) the strict requirements in terms of end-to-end delays, (ii) the required reliability in the control plane and (iii) the minimization of the energy consumption. To cope with these daunting issues, we provide the following main contributions. As first contribution, we address the problem of the optimal placement of SDN controllers. Specifically, we give a detailed analysis of the impact that controller placement imposes on the reactivity of SDN control plane, due to the consistency protocols adopted to manage the data structures that are shared across different controllers. We compute the Pareto frontier, showing all the possible tradeoffs achievable between the inter-controller delays and the switch-to-controller latencies. We define two data-ownership models and formulate the controller placement problem with the goal of minimizing the reaction time of control plane, as perceived by a switch. We propose two evolutionary algorithms, namely Evo-Place and Best-Reactivity, to compute the Pareto frontier and the controller placement minimizing the reaction time, respectively. Experimental results show that Evo-Place outperforms its random counterpart, and Best-Reactivity can achieve a relative error of <= 30% with respect to the optimal algorithm by only sampling less than 10% of the whole solution space. As second contribution, we propose a stateful SDN approach to improve the scalability of traffic classification in SDN networks. In particular, we leverage the OpenState extension to OpenFlow to deploy state machines inside the switch and minimize the number of packets redirected to the traffic classifier. We experimentally compare two approaches, namely Simple Count-Down (SCD) and Compact Count-Down (CCD), to scale the traffic classifier and minimize the flow table occupancy. As third contribution, we propose an approach to improve the reliability of SDN controllers. We implement BeCheck, which is a software framework to detect ``misbehaving'' controllers. BeCheck resides transparently between the control plane and data plane, and monitors the exchanged OpenFlow traffic messages. We implement three policies to detect misbehaving controllers and forward the intercepted messages. BeCheck along with the different policies are validated in a real test-bed. As fourth contribution, we investigate a mobile gaming scenario in the context of fog computing, denoted as Integrated Mobile Gaming (IMG) scenario. We partition mobile games into individual tasks and cognitively offload them either to the cloud or the neighbor mobile devices, so as to achieve minimal energy consumption. We formulate the IMG model as an ILP problem and propose a heuristic named Task Allocation with Minimal Energy cost (TAME). Experimental results show that TAME approaches the optimal solutions while outperforming two other state-of-the-art task offloading algorithms

Flexible architecture for the future internet scalability of SDN control plane

Software-Defined Networking (SDN) separates the control plane from the data plane. The initial SDN approach involves a single centralized controller, which may not scale properly as a network grows in size. Distributed controllers have emerged to address the disadvantages of a single centralized controller. The control architecture needs to be distributed with traffic control between switches and controllers and among the controllers in order to allow SDNs for several thousand switches. One of the most significant research challenges for distributed controller architectures is to effectively manage controllers, which includes allocating enough controllers to appropriate network locations. To address these daunting issues, we make the following major contributions: This thesis expands the method of solving the Control Placement Problem (CPP) based on the K-means and K-center algorithms to include a Hierarchical Controller Placement Problem (HCPP), located at a high level of Super Controller (SC), a middle level of Master Controllers (MCs), and the lowest level of domain controllers (DCs). The optimization metric addresses latency between the controller and the switches assigned to it.. The proposed architecture and methodology are implemented using the topology of Western European NRENs from the Internet Topology Zoo. The entire network topology is divided into clusters, and the optimal number of controllers (DCs) and their placement are determined for each cluster. MC placement optimization determines the optimal number of MCs and their optimal placement. As a second contribution, an accumulated latency is defined to solve CPP, which takes into account both the latency between the controller and its associated switches and the latency between controllers. Under the constraint of latency, an optimization problem is formulated as per mixed-integer linear programming (MILP). The goal of the research is to reduce accumulated latency while also reducing the number of network controllers and optimizing their placement to achieve an optimal balance. The performance of the developed method is evaluated on Internet2 OS3E real network topology. To achieve the third objective, a metric was developed that includes reliability. The communication latency between controllers should also be considered because a low controller-switch delay does not always imply a short controller-controller delay for a particular controller placement. As the third contribution, we propose a novel metric for CPP to improve the reliability of controllers that takes into account both communication latency and communication reliability between switches and controllers, as well as between controllers. When a single link fails, reliability is taken into account. This aspect concluded by identifying the optimal controller placement to achieve low latencies in control plane traffic. The goal of this project is to reduce the average latency. As the fourth contribution, this study evaluates the Joint Latency and Reliability-aware Controller Placement (LRCP) optimization model. As the evaluation metric, control plane latency (CPL) is defined as the sum of the average switch-to-controller latency and average inter-controller latency. The latency of the control plane, utilizing the actual latencies of the real network topology, is calculated for every optimum placement in the network. In the case of a failure of the single link, the actual CPL for LRCP placements is calculated and evaluated to determine how good LRCP placements are. CPL metrics are used to compare latency and reliability metrics with other models. This study provides proof that the developed methodologies for large-scale networks are highly powerful in terms of searching for all feasible controller placements while assessing the outcomes. In addition, compared to previous work including latency among controllers and reliability for an event of single-link failure.La xarxa definida per programari (SDN) separa el pla de control del pla de dades. L’enfocament SDN inicial implica un únic controlador centralitzat, que pot no escalar correctament a mesura que la xarxa creixi de mida. Els controladors distribuïts han sorgit per abordar els inconvenients d’un únic controlador centralitzat. . Un dels reptes de recerca més importants per a les arquitectures de controladors distribuïts és gestionar de manera eficaç els controladors, que inclou l’assignació de controladors suficients a les ubicacions de xarxa adequades. Per abordar aquests problemes, fem les següents contribucions. Aquesta tesi amplia el mètode de resolució del Problema de Col·locació de Control (CPP) basat en els algorismes de K-means i K-center per incloure un Problema de Col·locació de Controladors Jeràrquics (HCPP), situat a un nivell alt de Super Controller (SC), un nivell de controladors mestres (MC) i el nivell més baix de controladors de domini (DC). La mètrica d’optimització és la latència entre el controlador i els commutadors assignats a aquest. L’arquitectura i la metodologia proposades s’implementen utilitzant la topologia de NREN d’Europa occidental de l’Internet Topology Zoo. La topologia de la xarxa es divideix en clústers i es determina el nombre òptim de controladors de domini (DC) i la seva ubicació per a cada clúster. L’optimització de la ubicació de MC determina el nombre òptim de MC i la seva col·locació òptima. Com a segona contribució, es defineix una latència acumulada per resoldre el CPP, que té en compte tant la latència entre el controlador i els seus commutadors associats com la latència entre controladors. Sota la restricció de la latència, es formula un problema d’optimització segons la programació lineal de nombres enters mixts (MILP). L’objectiu de la investigació és reduir la latència acumulada alhora que es redueix el nombre de controladors de xarxa i optimitza la seva col·locació per aconseguir un equilibri òptim. El rendiment del mètode desenvolupat s’avalua en la topologia de xarxa real d’Internet2 OS3E. Per aconseguir el tercer objectiu, es va desenvolupar una mètrica que inclou la fiabilitat. També s’ha de tenir en compte la latència de comunicació entre controladors perquè un retard baix entre el commutador i el controlador no sempre implica un retard curt del controladorcontrolador per a una ubicació concreta dels controladors. Com a tercera contribució, proposem una nova mètrica per al CPP per millorar la fiabilitat dels controladors que tingui en compte tant la latència de la comunicació com la fiabilitat de la comunicació entre commutadors i controladors, així com entre controladors. La fiabilitat es té en compte quan falla un únic enllaç identificant la col·locació òptima dels controladors per aconseguir baixes latències en el trànsit del pla de control. L’objectiu d’aquest projecte és reduir la latència mitjana. Com a quarta contribució, aquest estudi avalua el model d’optimització Joint Latency and Reliability-aware Controller Placement (LRCP). Com a mètrica d’avaluació, la latència del pla de control (CPL) es defineix com la suma de la latència mitjana de commutador a controlador i la latència mitjana entre controladors. La latència del pla de control, utilitzant les latències reals de la topologia de xarxa real, es calcula per a cada col·locació òptima a la xarxa. En el cas d’una fallida en un únicenllaç, es calcula i s’avalua el CPL real de les ubicacions LRCP per determinar com de bones són les ubicacions LRCP. Les mètriques CPL s’utilitzen per comparar les mètriques de latència i fiabilitat amb altres models. Aquest estudi proporciona la prova que les metodologies desenvolupades per a xarxes a gran escala són molt potents pel que fa a la recerca de totes les ubicacions de controladors factibles mentre s’avaluen els resultats. A més, en comparació amb el treball anterior, inclou la latència entre els controladors i la fiabilitat per a un esdeveniment de fallada d’un enllaç únic.Las redes definidas por software (SDN) separan el plano de control del plano de datos. El enfoque inicial de SDN implica un único controlador centralizado, que puede no escalar adecuadamente a medida que una red crece en tamaño. Los controladores distribuidos han surgido para abordar las desventajas de un único controlador centralizado. Uno de los retos de investigación más importantes para las arquitecturas de controladores distribuidos es la gestión eficaz de los controladores, que incluye la asignación de suficientes controladores en las ubicaciones adecuadas. Para hacer frente a estos problemas, realizamos las siguientes contribuciones principales: Esta tesis amplía el método de resolución del Problema de Colocación de Controles (CPP) basado en los algoritmos K-means y K-center para incluir un Problema de Colocación de Controladores Jerárquicos (HCPP), situado en un nivel alto de Super-controladores (SC), un nivel medio de Controladores Maestros (MC), y el nivel más bajo de controladores de dominio (DC). La métrica de optimización es la latencia entre el controlador y los conmutadores asignados al mismo. . La arquitectura y la metodología propuestas se implementan utilizando la topología de las NREN de Europa Occidental del TopologyZoo. La topología completa de la red se divide en clústeres, y se determina el número óptimo de controladores de dominio (CD) y su colocación para cada clúster. La optimización de la colocación de los MC determina el número óptimo de MC y su colocación óptima. Como segunda contribución, se define una latencia acumulada para resolver el CPP, que tiene en cuenta tanto la latencia entre el controlador y sus conmutadores asociados como la latencia entre los controladores. Bajo la restricción de la latencia, se formula un problema de optimización según la programación lineal de enteros mixtos (MILP). El objetivo es reducir la latencia acumulada al tiempo que se reduce el número de controladores de la red y se optimiza su ubicación para lograr un equilibrio óptimo. El rendimiento del método desarrollado se evalúa en la topología de Internet2 OS3E. Para lograr el tercer objetivo, se desarrolló una métrica que incluye la fiabilidad. La latencia de la comunicación entre controladores también debe tenerse en cuenta, ya que un bajo retardo entre controladores y conmutadores no siempre implica un corto retardo entre controladores para una determinada ubicación de los mismos. Como tercera contribución proponemos una nueva métrica para el CPP para mejorar la fiabilidad de los controladores que tiene en cuenta tanto la latencia de la comunicación como la fiabilidad de la comunicación entre los conmutadores y los controladores, así como entre los controladores. Se tiene en cuenta la fiabilidad cuando falla un solo enlace. Este aspecto concluye con la identificación de la ubicación óptima de los controladores para lograr bajas latencias en el tráfico del plano de control. El objetivo es reducir la latencia media. Como cuarta contribución, este estudio evalúa el modelo de optimización Joint Latency and Reliability-aware Controller Placement (LRCP). Como métrica de evaluación, la latencia del plano de control (CPL) se define como la suma de la latencia media entre conmutadores y controladores y la latencia media entre controladores. La latencia del plano de control, utilizando las latencias reales de la topología de la red, se calcula para cada ubicación óptima en la red. En el caso de un fallo de un enlace, se calcula y evalúa la CPL real para las colocaciones de LRCP con el fin de determinar lo buenas que son las colocaciones de LRCP. Las métricas CPL se utilizan para comparar las métricas de latencia y fiabilidad con otros modelos. Este estudio demuestra que las metodologías desarrolladas para redes a gran escala son muy potentes en cuanto a la búsqueda de todas las ubicaciones factibles de los controladores mientras se evalúan los resultados. Además, en comparación con los trabajos anteriores, que incluyen la latencia entre controladores y la fiabilidad para un caso de fallo de un solo enlacePostprint (published version

Deployment of NFV and SFC scenarios

Aquest ítem conté el treball original, defensat públicament amb data de 24 de febrer de 2017, així com una versió millorada del mateix amb data de 28 de febrer de 2017. Els canvis introduïts a la segona versió són 1) correcció d'errades 2) procediment del darrer annex.Telecommunications services have been traditionally designed linking hardware devices and providing mechanisms so that they can interoperate. Those devices are usually specific to a single service and are based on proprietary technology. On the other hand, the current model works by defining standards and strict protocols to achieve high levels of quality and reliability which have defined the carrier-class provider environment. Provisioning new services represent challenges at different levels because inserting the required devices involve changes in the network topology. This leads to slow deployment times and increased operational costs. To overcome the current burdens network function installation and insertion processes into the current service topology needs to be streamlined to allow greater flexibility. The current service provider model has been disrupted by the over-the-top Internet content providers (Facebook, Netflix, etc.), with short product cycles and fast development pace of new services. The content provider irruption has meant a competition and stress over service providers' infrastructure and has forced telco companies to research new technologies to recover market share with flexible and revenue-generating services. Network Function Virtualization (NFV) and Service Function Chaining (SFC) are some of the initiatives led by the Communication Service Providers to regain the lost leadership. This project focuses on experimenting with some of these already available new technologies, which are expected to be the foundation of the new network paradigms (5G, IOT) and support new value-added services over cost-efficient telecommunication infrastructures. Specifically, SFC scenarios have been deployed with Open Platform for NFV (OPNFV), a Linux Foundation project. Some use cases of the NFV technology are demonstrated applied to teaching laboratories. Although the current implementation does not achieve a production degree of reliability, it provides a suitable environment for the development of new functional improvements and evaluation of the performance of virtualized network infrastructures