A survey of feature selection in Internet traffic characterization

In the last decade, the research community has focused on new classification methods that rely on statistical characteristics of Internet traffic, instead of pre-viously popular port-number-based or payload-based methods, which are under even bigger constrictions. Some research works based on statistical characteristics generated large fea-ture sets of Internet traffic; however, nowadays it?s impossible to handle hun-dreds of features in big data scenarios, only leading to unacceptable processing time and misleading classification results due to redundant and correlative data. As a consequence, a feature selection procedure is essential in the process of Internet traffic characterization. In this paper a survey of feature selection methods is presented: feature selection frameworks are introduced, and differ-ent categories of methods are briefly explained and compared; several proposals on feature selection in Internet traffic characterization are shown; finally, future application of feature selection to a concrete project is proposed

A novel P2P and cloud computing hybrid architecture for multimedia streaming QoS cost functions

Since its appearance, peer-to-peer technology has given raise to various multimedia streaming applications. Today, cloud computing offers different service models as a base for successful end user applications. In this paper we propose joining peer-to-peer and cloud computing into new architectural realization of a distributed cloud computing network for multimedia streaming, in a both centralized and peer-to-peer distributed manner. This architecture merges private and public clouds and it is intended for a commercial use, but in the same time scalable to offer the possibility of non-profitable use. In order to take advantage of the cloud paradigm and make multimedia streaming more efficient, we introduce APIs in the cloud, containing build-in functions for automatic QoS calculation, which permits negotiating QoS parameters such as bandwidth, jitter and latency, among a cloud service provider and its potential clients

SLBN: A Scalable Max-min Fair Algorithm for Rate-Based Explicit Congestion Control

The growth of the Internet has increased the need for scalable congestion control mechanisms in high speed networks. In this context, we propose a rate-based explicit congestion control mechanism with which the sources are provided with the rate at which they can transmit. These rates are computed with a distributed max-min fair algorithm, SLBN. The novelty of SLBN is that it combines two interesting features not simultaneously present in existing proposals: scalability and fast convergence to the max-min fair rates, even under high session churn. SLBN is scalable because routers only maintain a constant amount of state information (only three integer variables per link) and only incur a constant amount of computation per protocol packet, independently of the number of sessions that cross the router. Additionally, SLBN does not require processing any data packet, and it converges independently of sessions' RTT. Finally, by design, the protocol is conservative when assigning rates, even in the presence of high churn, which helps preventing link overshoots in transient periods. We claim that, with all these features, our mechanism is a good candidate to be used in real deployments

Brief Announcement: Node Sampling Using Centrifugal Random Walks.

We propose distributed algorithms for sampling networks based on a new class of random walks that we call Centrifugal Random Walks (CRW). A CRW is a random walk that starts at a source and always moves away from it. We propose CRW algorithms for connected networks with arbitrary probability distributions, and for grids and networks with regular concentric connectivity with distance based distributions. All CRW sampling algorithms select a node with the exact probability distribution, do not need warm-up, and end in a number of hops bounded by the network diameter

Testing a Cloud Provider Network for Hybrid P2P and Cloud Streaming Architectures

The number of online real-time streaming services deployed over network topologies like P2P or centralized ones has remarkably increased in the recent years. This has revealed the lack of networks that are well prepared to respond to this kind of traffic. A hybrid distribution network can be an efficient solution for real-time streaming services. This paper contains the experimental results of streaming distribution in a hybrid architecture that consist of mixed connections among P2P and Cloud nodes that can interoperate together. We have chosen to represent the P2P nodes as Planet Lab machines over the world and the cloud nodes using a Cloud provider's network. First we present an experimental validation of the Cloud infrastructure's ability to distribute streaming sessions with respect to some key streaming QoS parameters: jitter, throughput and packet losses. Next we show the results obtained from different test scenarios, when a hybrid distribution network is used. The scenarios measure the improvement of the multimedia QoS parameters, when nodes in the streaming distribution network (located in different continents) are gradually moved into the Cloud provider infrastructure. The overall conclusion is that the QoS of a streaming service can be efficiently improved, unlike in traditional P2P systems and CDN, by deploying a hybrid streaming architecture. This enhancement can be obtained by strategic placing of certain distribution network nodes into the Cloud provider infrastructure, taking advantage of the reduced packet loss and low latency that exists among its datacenters

A telecom analytics framework for dynamic quality of service management

Since the beginning of Internet, Internet Service Providers (ISP) have seen the need of giving to users? traffic different treatments defined by agree- ments between ISP and customers. This procedure, known as Quality of Service Management, has not much changed in the last years (DiffServ and Deep Pack-et Inspection have been the most chosen mechanisms). However, the incremen-tal growth of Internet users and services jointly with the application of recent Ma- chine Learning techniques, open up the possibility of going one step for-ward in the smart management of network traffic. In this paper, we first make a survey of current tools and techniques for QoS Management. Then we intro-duce clustering and classifying Machine Learning techniques for traffic charac-terization and the concept of Quality of Experience. Finally, with all these com-ponents, we present a brand new framework that will manage in a smart way Quality of Service in a telecom Big Data based scenario, both for mobile and fixed communications

La plataforma iTecSoft: Un caso de colaboración inter-organizativa 2.0.

El artículo describe la metodología utilizada para abordar el problema de la colaboración intra- e inter-organizativa mediante la descripción del caso específico del proyecto ITECBAN. En este caso, la colaboración se describe en los términos de la organización virtual y el problema se afronta bajo parámetros de flexibilidad a la hora de definir y gestionar esa organización virtual, formada por equipos de trabajo y profesionales geográfica y organizativamente dispersos. La solución planteada, la plataforma Itecsoft, se apoya en una arquitectura abierta de colaboración y una serie de componente de middleware y servicios que acaban tomando sustanciándose en un conjunto de herramientas de código libre en su mayoría provenientes de proyectos y comunidades que han surgido o se han reactivado al calor del fenómeno “dos-punto-cero”. Las principales aportaciones de la solución propuesta son la resolución del problema de la identidad en el entorno distribuido y descentralizado de colaboración de ITECBAN, junto con la selección, evaluación e integración de las herramientas de colaboración dentro de esa arquitectura abierta

Contribución a la distribución de contenidos multimedia sobre redes peer-to-peer

Los sistemas cooperativos se despliegan sobre solapamientos de red con el objetivo de compartir ancho de banda, un recurso que hasta el momento no ha sido compartido masivamente. Estos sistemas construyen un solapamiento de red para coordinar los diferentes nodos y una red de distribución para el contenido. En los sistemas cooperativos actuales (SplitStream/Scribe/Pastry y Bayeux/ Tapestry), las dos redes comparten la misma topología. Siendo el árbol de distribución del contenido, un subconjunto de la topología de la red de control. El árbol de distribución es fácil de construir, pero fuerza a participar en el árbol de distribución ánodos intermedios no interesados en la recepción del flujo multimedia. En un escenario de nodos heterogéneos en cuanto a ancho de banda se refiere, el ancho de banda de suida necesario para reenviar el contenido a N hijos, puede no estar disponible en los nodos no interesados. Además, este escenario puede no ser aceptable debido a que los nodos forzados a participar en el reenvío, generan copias adicionales del flujo multimedia en la red física. Cuando el número de nodos interesados es un subconjunto de todos los nodos conectados, si construimos un árbol específico de distribución aparece una pequeña sobrecarga durante la construcción. Sin embargo, si construimos el árbol de distribución usando la red de control, e incluyendo a los nodos no interesados en recibir el flujo, entonces una gran cantidad de copias extra del flujo multimedia serán retransmitidas en la red física. Por lo tanto cuando el flujo multimedia consume un ancho de banda significativo (por ejemplo video o audio en formato streaming), la primera solución es más efectiva desde el punto de vista de la red y de los nodos. Nuestra solución permite construir árboles específicos con una topología independiente de la de la red de control. Los árboles solo se construyen por los nodos interesados en el flujo multimedia, y éstos pueden ser totalmente heterogéneos en cuanto a sus recursos (principalmente ancho de banda). El proceso de unión al árbol es totalmente distribuido y altamente escalable, generando una sobrecarga baja en la red. La construcción del árbol se realiza de una forma totalmente distribuida y escalable, publicando los nodos ya enganchados al árbol la información sobre la topología actual de la red , y segundo, mediante la localización por parte de los nuevos nodos de esa información. La publicación y localización de huecos se puede construir sobre el solapamiento Chord usando la primitiva de almacenamiento de pares y una ligeramente modificada primitiva de búsqueda. Asimismo nuestra solución puede ser aplicada para construir árboles multiflujo. Cooperative systems are deployed over overlay networks in order to share a resource, bandwidth, which up to now has not been widely shared. Those systems build an overlay control network, for the coordination algorithm among the different peers, and a distribution network, for the contení. In the most popular fully distributed cooperative systems (like in Split- Stream/Scribe/Pastry and Bayeux/Tapestry) both networks share the same topology, being the content distribution spanning tree topology a subset of the control network one. The spanning tree building is simple, but forces intermedíate nodes not interested in the reception of the muhimedia stream, to particípate in the spanning tree. In a fully heterogeneous bandwidth peer community scenario, the upload bandwidth needed to relay a multimedia stream to N children peers may be unavailable in these non-streaminterested peers. Besides, this scenario may not be acceptable because the nodes forced to particípate retransmit additional extra copies of multimedia stream to the physical network When the number of stream interested peers is a subset of the total number of peers connected in the control network, if we build a specific spanning tree with only interested peers then a small tree building overhead appears. However, if we build the spanning tree using the overlay control network topology and including nodes not interested in receiving the stream, then a big amount of extra copies of multimedia stream are retransmitted to the physical network. Therefore, when the multimedia stream transmission consumes a médium or high bit rate (e.g. video or audio streaming) the former solution performs better from an overall (network and peers) point of view. Our solution allows building efficient and specific data distribution spanning trees with an independent topology from the control network one. Trees are built only by the peers interested in the stream, and the tree components can be totally heterogeneous in their resource (mainly bandwidth) capabilities. The joining process is fully distributed and highly scalable generating low network overhead. The tree building is performed in a fully distributed and scalable fashion, first by publishing Information about the actual tree topology by the peers joined to the tree, and second by locating this data by the new peers interested in joining the spanning tree. The publishing and locating data procedures can be built on top of a Chord overlay control network by using the typical store (key,value) primitive, and a slightly modifíed search (key) primitive available in their DHT (Distributed Hash Table) systems. Also our solution can be applied to build multiflow spanning trees

Regularized greedy column subset selection

The Column Subset Selection Problem is a hard combinatorial optimization problem that provides a natural framework for unsupervised feature selection, and there exist efficient algorithms that provide good approximations. The drawback of the problem formulation is that it incorporates no form of regularization, and is therefore very sensitive to noise when presented with scarce data. In this paper we propose a regularized formulation of this problem, and derive a correct greedy algorithm that is similar in efficiency to existing greedy methods for the unregularized problem. We study its adequacy for feature selection and propose suitable formulations. Additionally, we derive a lower bound for the error of the proposed problems. Through various numerical experiments on real and synthetic data, we demonstrate the significantly increased robustness and stability of our method, as well as the improved conditioning of its output, all while remaining efficient for practical use