ALEVIN - A framework to develop, compare, and analyze virtual network embedding algorithms

Network virtualization is recognized as an enabling technology for the Future Internet. Applying virtualization of network resources leads to the problem of mapping virtual resources to physical resources, known as “Virtual Network Embedding” (VNE). Several algorithms attempting to solve this problem have been discussed in the literature, so far. However, comparison of VNE algorithms is hard, as each algorithm focuses on different criteria. To that end, we introduce a framework to compare different algorithms according to a set of metrics, which allow to evaluate the algorithms and compute their results on a given scenario for arbitrary parameters.Peer ReviewedPostprint (published version

Study, evaluation and contributions to new algorithms for the embedding problem in a network virtualization environment

Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change and to enable a new business model decoupling the network services from the underlying infrastructure. The problem of embedding virtual networks in a substrate network is the main resource allocation challenge in network virtualization and is usually referred to as the Virtual Network Embedding (VNE) problem. VNE deals with the allocation of virtual resources both in nodes and links. Therefore, it can be divided into two sub-problems: Virtual Node Mapping where virtual nodes have to be allocated in physical nodes and Virtual Link Mapping where virtual links connecting these virtual nodes have to be mapped to paths connecting the corresponding nodes in the substrate network. Application of network virtualization relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as VNE algorithms. This thesis proposes a set of contributions to solve the research challenges of the VNE that have not been tackled by the research community. To do that, it performs a deep and comprehensive survey of virtual network embedding. The first research challenge identified is the lack of proposals to solve the virtual link mapping stage of VNE using single path in the physical network. As this problem is NP-hard, existing proposals solve it using well known shortest path algorithms that limit the mapping considering just one constraint. This thesis proposes the use of a mathematical multi-constraint routing framework called paths algebra to solve the virtual link mapping stage. Besides, the thesis introduces a new demand caused by virtual link demands into physical nodes acting as intermediate (hidden) hops in a path of the physical network. Most of the current VNE approaches are centralized. They suffer of scalability issues and provide a single point of failure. In addition, they are not able to embed virtual network requests arriving at the same time in parallel. To solve this challenge, this thesis proposes a distributed, parallel and universal virtual network embedding framework. The proposed framework can be used to run any existing embedding algorithm in a distributed way. Thereby, computational load for embedding multiple virtual networks is spread across the substrate network Energy efficiency is one of the main challenges in future networking environments. Network virtualization can be used to tackle this problem by sharing hardware, instead of requiring dedicated hardware for each instance. Until now, VNE algorithms do not consider energy as a factor for the mapping. This thesis introduces the energy aware VNE where the main objective is to switch off as many network nodes and interfaces as possible by allocating the virtual demands to a consolidated subset of active physical networking equipment. To evaluate and validate the aforementioned VNE proposals, this thesis helped in the development of a software framework called ALgorithms for Embedding VIrtual Networks (ALEVIN). ALEVIN allows to easily implement, evaluate and compare different VNE algorithms according to a set of metrics, which evaluate the algorithms and compute their results on a given scenario for arbitrary parameters

Coordinated node and link mapping VNE using a new paths algebra strategy

AbstractThe main resource allocation research challenge in network virtualization is the Virtual Network Embedding (VNE) Problem. It consists of two stages, usually performed separately: node mapping and link mapping. A new mathematical multi-constraint routing framework for linear and non-linear metrics called “paths algebra” has already been proposed to solve the second stage, providing good results thanks to its flexibility. Unlike existing approaches, paths algebra is able to include any kind of network parameters (linear and non-linear) to solve VNE in reasonable runtime. While paths algebra had only been used to solve one stage (link mapping) of the VNE, this paper suggests an improvement to solve VNE using the paths algebra-based strategy by coordinating, in a single stage, the mapping of nodes and links based on a ranking made of the bi-directional pair of nodes of the substrate network, ordered by their available resources. Simulation results show that the New Paths Algebra-based strategy shows significant performance improvements when compared against the uncoordinated paths Algebra-based link mapping approach

Network Virtualization strategy based on Paths Algebra to implement the concept of Infrastructure as a Service

One of the main challenges of network virtualization is the virtual network embedding problem(VNE). The objective of the VNE is to map a set of Virtual Network Request (VNR) to a physical node and link. VNR is composed by a set of virtual nodes and links with several demands (Processing power, Bandwidth.), which they need to be assigned into a set of paths in the substrate network with sufficient resources to accomplish their demands. Furthermore, these embedding can be optimized with regard to several parameters, such as: embedding cost, link bandwidth, energy- efficiency, packet loss rate, throughput, etc. To solve the VNE program a mathematical tools was proposed, called “Paths algebra”. This framework can helped solve the multiple multi- constraint routing problems of VNE using linear metrics as bandwidth, number of hops and delay, or non-linear metrics as availability and package loss rate Most of the existing VNE proposals treat the single-path virtual link-mapping problem as a mono-constraint, that is, their objective is to map the virtual links in substrate paths that minimize/maximize the usage of one resource (typically bandwidth). This paper introduces a virtual link mapping approach supporting multiple constraints using the “paths algebra” routing framewor

Requeriments en intel·ligència artificial per l’allotjament eficient de xarxes virtuals

Network virtualization is becoming increasingly important, as it allows to adapt the needs of a network to new circumstances, resulting in greater flexibility. Network virtualization depends mostly on the optimization of the allocation of physical network resources, becoming one of the major problems of today. This problem is called virtual network embedding (VNE), many algorithms have been used to solve this virtual network embedding problem with poor results in terms of revenue. Due to the current rise of artificial intelligence, it has been used in order to solve technological problems of recent years, the use of the Q learning algorithm, a typical algorithm for reinforcing learning, is proposed. The results achieved show an improvement in the use of resources which allows to obtain better values in terms of Cost/Revenue, compared to other traditional algorithms.La virtualización de redes está ganando cada vez más importancia, ya que permite adecuar las necesidades de una red a nuevas circunstancias, lo que deriva en una flexibilidad mayor. La virtualización de la red depende en gran parte de la optimización en la asignación de recursos de la red física, lo que lo convierte en uno de los grandes problemas de la actualidad. Este problema recibe el nombre de alojamiento de redes virtuales (VNE), muchos algoritmos se han utilizado para resolver este problema de alojamiento de recursos obteniendo resultados pobres en términos de Revenue. Debido al auge que hay en la actualidad con la inteligencia artificial como medio para resolver los problemas tecnológicos de los últimos años, se propone la utilización del algoritmo Q learning, un algoritmo típico del aprendizaje por refuerzo. Los resultados obtenidos demuestran una mejora en la utilización de recursos, lo que permite obtener mejores valores en términos de Cost/Revenue comparado con otros algoritmos tradicionales.La virtualització de xarxes està guanyant cada cop més importància, ja que permet adequar les necessitats d'una xarxa a noves circumstancies, el que deriva en una flexibilitat major. La virtualització de la xarxa depèn en gran mesura de l'optimització en l'assignació dels recursos de la xarxa física, esdevenint en un dels grans problemes de l'actualitat. Aquest problema rep el nom d'allotjament de xarxes virtuals (VNE), molts algorismes s'han utilitzat per resoldre aquest problema d'allotjament de recursos obtenint resultats pobres en termes de revenue. Degut a l'auge que hi ha en l'actualitat amb la intel·ligència artificial com a medi per a resoldre els problemes tecnològics dels últims anys, és proposa la utilització de l'algorisme Q learning, un algorisme típic de l'aprenentatge de reforç. Els resultats obtinguts demostren una millora en la utilització de recursos, el que permet obtenir millors valors en termes de Cost/Revenue, comparat amb altres algorismes tradicionals

Location-based Data Model for Optimized Network Slice Placement

International audienceNetwork Slicing has its roots in Network Function Virtualization (NFV) allowing high flexibility in the delivery of end-to-end network services. To achieve Network Slicing promises on efficiency, Network Slice Providers have to ensure optimized resource utilization and to guarantee Quality of Service when managing the life-cycle of a Network Slice. We focus in this paper on Network Slice Placement, intimately related to the VNF Placement and Chaining problem. In contrary to most studies related to VNF placement, we deal with the most complete and complex Network Slice topologies and we pay special attention to the geographic location of Network Slice Users. We propose a data model adapted to Integer Linear Programming. Extensive numerical experiments assess the relevance of taking into account the user location constraints

Heuristic for Edge-enabled Network Slicing Optimization using the "Power of Two Choices"

International audienceWe propose an online heuristic algorithm for the problem of network slice placement optimization. The solution is adapted to support placement on large scale networks and integrates Edge-specific and URLLC constraints. We rely on an approach called the "Power of Two Choices" to build the heuristic. The evaluation results show the good performance of the heuristic that solves the problem in few seconds under a large scale scenario. The heuristic also improves the acceptance ratio of network slice placement requests when compared against a deterministic online Integer Linear Programming (ILP) solution

Asignación eficiente de recursos en centros de datos virtualizados abordando el compromiso entre tolerancia a fallos y consumo energético

RESUMEN: Los centros de datos han venido ganando mucho interés debido a su capacidad para almacenar una gran cantidad de información; como resultado se han convertido en el mayor soporte para la computación en la nube. Actualmente, los centros de datos presentan problemas de rendimiento, como: calidad de servicio no garantizada, riesgos de seguridad, complejidad en la administración e inflexibilidad. Los centros de datos virtualizados son una herramienta emergente para enfrentar estos problemas. Pero su implementación trae incluso un reto mayor: ¿Cómo asignar óptimamente las demandas de los centros de datos virtuales sobre la infraestructura física, de tal manera que se reduzcan los costos de operación del Centro de Datos, se mejoren las ganancias y se garanticen los acuerdos de servicio con los usuarios finales? Este reto es conocido como “Virtual Data Center Embedding”' (VDCE, siglas en inglés). Este manuscrito inicialmente presenta un estado del arte de la investigación en este problema, y también propone una clasificación taxonómica de las propuestas principales para resolverlo. Esta revisión nos ha permitido encontrar que no se ha propuesto una solución que estudie el comportamiento del proceso del VDCE cuando se busca reducir los recursos necesarios para la tolerancia a fallos y minimizar el consumo energético en forma coordinada. En esta investigación se presenta una propuesta para abordar el VDCE, basados en un programa lineal entero mixto (MILP, siglas en inglés) multiobjetivo, que se soluciona utilizando el método de las €-restricciones con el que se pueden obtener respuestas no dominadas. Nuestra propuesta soluciona el VDCE buscando el equilibrio entre la minimización del consumo energético y el garantizar supervivencia a fallos en los servidores de la red del centro de datos físico. Para comprobar el desempeño implementamos nuestra propuesta de forma simulada en java utilizando las funciones proporcionadas por las herramientas ALEVIN y Gurobi. Los resultados del experimento demuestran que nuestro modelo tiene mejor desempeño cuando se le dio más importancia a minimizar los recursos de respaldo necesarios para garantizar una completa tolerancia a fallos. Los resultados también demuestran que el modelo propuesto en esta investigación tiene una aceptación de redes virtuales que supera el 8% cuando se compara con un algoritmo existente

Using Social Media to Evaluate Public Acceptance of Infrastructure Projects

The deficit of infrastructure quality of the United States demands groundbreaking of more infrastructure projects. Despite the potential economic and social benefits brought by these projects, they could also negatively impact the community and the environment, which could in turn affect the implementation and operation of the projects. Therefore, measuring and monitoring public acceptance is critical to the success of infrastructure projects. However, current practices such as public hearings and opinion polls are slow and costly, hence are insufficient to provide satisfactory monitoring mechanism. Meanwhile, the development of state-of-the-art technologies such as social media and big data have provided people with unprecedented ways to express themselves. These platforms generate huge volumes of user-generated content, and have naturally become alternative sources of public opinion. This research proposes a framework and an analysis methodology to use big data from social media (e.g. the microblogging site Twitter) for project evaluation. The framework collects social media postings, analyzes public opinion towards infrastructure projects and builds multi-dimensional models around the big data. The interface and conceptual implementation of each component of the framework are discussed. This framework could be used as a supplement to traditional polls to provide a fast and cost-effective way for public opinion and project risk assessment. This research is followed by a case study applying the framework to a real-world infrastructure project to demonstrate the feasibility and comprehensiveness of the framework. The California High Speed Rail project is selected to be the object of study. It is an iconic and controversial large-scale infrastructure project that faced a lot of criticism, complaints and suggestions. Sentiment analysis, the most important type of analysis on the framework, is discussed concerning its application and implementation in the context of infrastructure projects. A public acceptance model for social media sentiment analysis is proposed and examined, and the best measurement of public acceptance is recommended. Moreover, the case study explores the driving force of the change in public acceptance: the social media events. Events are defined, evaluated, and an event influence quadrant is proposed to categorize and prioritize social media events. Furthermore, the individuals influencing the perceptions of these events, opinion leaders, are also modeled and identified. Three opinion leadership types are defined with top users in each type listed and discussed. A predictive model for opinion leader is also developed to identify opinion leaders using an a priori indicator. Finally, a user profiling model is established to describe social demographic characteristics of users, and each demographic feature is discussed in detail