52 research outputs found

    Performance Evaluation of MBAC solutions

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    Admission control prevents certain flows from accessing a network with regard to the current utilization level of its resources with the ultimate goal of avoiding congestion and performance collapses, so that, accepted flows receive a sufficient level of Quality of Service (QoS). In this paper, we evaluate the performance of three measurement-based admission control (MBAC) solutions in the context of semantic networks, which autonomously acquire a knowledge on the on-going traffic. Each MBAC solution is carefully parameterized to have identical performance target expressed in terms of maximum tolerable loss rate or either queueing delay. We also include the results that would be obtainable by an ideal admission control so as to benchmark the performance of existing MBAC. An extensive set of simulations, using different methods for representing the background traffic, viz. a Poisson process, a PPBP process and a real trace, were carried out to evaluate the performance of each solution. Our results tend to show that, in case of a target loss rate, when the characteristics of background traffic deviate significantly from a "regular" source (e.g., a Poisson process) to more variable sources (e.g., a PPBP process, a real trace), the "Aggregate Traffic Envelopes" and "Equivalent Capacity" solutions brings satisfactory results, much better than those obtained by the "Measured Sum" solution. However, if one deals with a queuing delay target, the outcomes of any MBAC solution are generally less successful, as they typically tend to deviate further from the ideal admission control, often in an overly conservative way

    KBAC: Knowledge-Based Admission Control

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    Many methods have been proposed in the literature to perform admission control in order to provide a sufficient level of Quality of Service (QoS) to accepted flows. In this paper, we introduce a novel data-driven method based on a time-varying model that we refer to as Knowledge-Based Admission Control solution (KBAC). Our KBAC solution consists of three main stages: (i) collect measurements on the on-going traffic over the communication link; (ii) maintain an up-to-date broad view of the link behavior, and feed it to a Knowledge Plane; (iii) model the observed link behavior by a mono-server queue whose parameters are set automatically and which predicts the expected QoS if a flow requesting admission were to be accepted. Our KBAC solution provides a probabilistic guarantee whose admission threshold is either expressed, as a bounded delay or as a bounded loss rate. We run extensive simulations to assess the behavior of our KBAC solution in the case of a delay threshold. The results show that our KBAC solution leads to a good trade-off between flow performance and resource utilization. This ability stems from the quick and automatic adjustment of its admission policy according to the actual variations on the traffic conditions

    Study of segmentation and identification techniques applied to environments with natural illumination and moving objects

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    La presente tesis está enmarcada en el área de visión por computador y en ella se realizan aportaciones encaminados a resolver el problema de segmentar automáticamente objetos en imágenes de escenas adquiridas en entornos donde se está realizando actividad, es decir, aparece movimiento de los elementos que la componen, y con iluminación variable o no controlada. Para llevar a cabo los desarrollos y poder evaluar prestaciones se ha abordado la resolución de dos problemas distintos desde el punto de vista de requerimientos y condiciones de entorno. En primer lugar se aborda el problema de segmentar e identificar, los códigos de los contenedores de camiones con imágenes tomadas en la entrada de un puerto comercial que se encuentra ubicada a la intemperie. En este caso se trata de proponer técnicas de segmentación que permitan extraer objetos concretos, en nuestro caso caracteres en contenedores, procesando imágenes individuales. No sólo supone un reto el trabajar con iluminación natural, sino además el trabajar con elementos deteriorados, con contrastes muy diferentes, etc. Dentro de este contexto, en la tesis se evalúan técnicas presentes en la literatura como LAT, Watershed, algoritmo de Otsu, variación local o umbralizado para segmentar imágenes en niveles de gris. A partir de este estudio, se propone una solución que combina varias de las técnicas anteriores, en un intento de abordar con éxito la extracción de caracteres de contenedores en todas las situaciones ambientales de movimiento e iluminación. El conocimiento a priori del tipo de objetos a segmentar nos permitió diseñar filtros con capacidad discriminante entre el ruido y los caracteres. El sistema propuesto tiene el valor añadido de que no necesita el ajuste de parámetros, por parte del usuario, para adaptarse a las variaciones de iluminación ambientales y consigue un nivel alto en la segmentación e identificación de caracteres.Rosell Ortega, JA. (2011). Study of segmentation and identification techniques applied to environments with natural illumination and moving objects [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10863Palanci

    A QoS-enabled resource management scheme for F-HMIPv6 micro mobility approach

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    In the near future, wireless networks will certainly run real-time applications with special Quality of Service (QoS) requirements. In this context micro mobility management schemes such as Fast Handovers over Hierarchical Mobile IPv6 (F-HMIPv6) will be a useful tool in reducing Mobile IPv6 (MIPv6) handover disruption and thereby to improve delay and losses. However, F-HMIPv6 alone does not support QoS requirements for real-time applications. Therefore, in order to accomplish this goal, a novel resource management scheme for the Differentiated Services (DiffServ) QoS model is proposed to be used as an add-on to F-HMIPv6. The new resource management scheme combines the F-HMIPv6 functionalities with the DiffServ QoS model and with network congestion control and dynamic reallocation mechanisms in order to accommodate different QoS traffic requirements. This new scheme based on a Measurement-Based Admission Control (MBAC) algorithm is effective, simple, scalable and avoids the well known traditional resource reservation issues such as state maintenance, signaling overhead and processing load. By means of the admission evaluation of new flows and handover flows, it is able to provide the desired QoS requirements for new flows while preserving the QoS of existing ones. The evaluated results show that all QoS metrics analyzed were significantly improved with the new architecture indicating that it is able to provide a highly predictive QoS support to F-HMIPv6

    Statistical multiplexing and connection admission control in ATM networks

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    Asynchronous Transfer Mode (ATM) technology is widely employed for the transport of network traffic, and has the potential to be the base technology for the next generation of global communications. Connection Admission Control (CAC) is the effective traffic control mechanism which is necessary in ATM networks in order to avoid possible congestion at each network node and to achieve the Quality-of-Service (QoS) requested by each connection. CAC determines whether or not the network should accept a new connection. A new connection will only be accepted if the network has sufficient resources to meet its QoS requirements without affecting the QoS commitments already made by the network for existing connections. The design of a high-performance CAC is based on an in-depth understanding of the statistical characteristics of the traffic sources
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