The Contributory Effect of Latency on the Quality of Voice Transmitted over the Internet

Deployment of Voice over Internet Protocol (VoIP) is rapidly growing worldwide due to the new services it provides and cost savings derived from using a converged IP network. However, voice quality is affected by bandwidth, delay, latency, jitter, packet loss e.t.c. Latency is the dominant factor that degrades quality of voice transfer. There is therefore strong need for a study on the effect of Latency with the view to improving Quality of Voice (QoV) in VoIP network. In this work, Poisson probability theorem, Markov Chain, Probability distribution theorems and Network performance metric were used to study the effect of latency on QoS in VoIP network. This is achieved by considering the effect of latency resulting from several components between two points in multiple networks. The NetQoS Latency Calculator, Net-Cracker Professional® for Modeling and Matlab/Simulink® for simulating network were tools used and the results obtained compare favourably well with theoretical facts

QoE over-the-top multimédia em redes sem fios

One of the goals of an operator is to improve the Quality of Experience (QoE) of a client in networks where Over-the-top (OTT) content is being delivered. The appearance of services like YouTube, Netflix or Twitch, where in the first case it contains more than 300 hours of video per minute in the platform, brings issues to the managed data networks that already exist, as well as challenges to fix them. Video traffic corresponds to 75% of the whole transmitted data on the Internet. This way, not only the Internet did become the ’de facto’ video transmission path, but also the general data traffic continues to exponentially increase, due to the desire to consume more content. This thesis presents two model proposals and architecture that aim to improve the users’ quality of experience, by predicting the amount of video in advance liable of being prefetched, as a way to optimize the delivery efficiency where the quality of service cannot be guaranteed. The prefetch is done in the clients’ closest cache server. For that, an Analytic Hierarchy Process (AHP) is used, where through a subjective method of attribute comparison, and from the application of a weighted function on the measured quality of service metrics, the amount of prefetch is achieved. Besides this method, artificial intelligence techniques are also taken into account. With neural networks, there is an attempt of selflearning with the behavior of OTT networks with more than 14.000 hours of video consumption under different quality conditions, to try to estimate the experience felt and maximize it, without the normal service delivery degradation. At last, both methods are evaluated and a proof of concept is made with users in a high speed train.Um dos objetivos de um operador é melhorar a qualidade de experiência do cliente em redes onde existem conteúdos Over-the-top (OTT) a serem entregues. O aparecimento de serviços como o YouTube, Netflix ou Twitch, onde no primeiro caso são carregadas mais de 300 horas de vídeo por minuto na plataforma, vem trazer problemas às redes de dados geridas que já existiam, assim como desafios para os resolver. O tráfego de vídeo corresponde a 75% de todos os dados transmitidos na Internet. Assim, não só a Internet se tornou o meio de transmissão de vídeo ’de facto’, como o tráfego de dados em geral continua a crescer exponencialmente, proveniente do desejo de consumir mais conteúdos. Esta tese apresenta duas propostas de modelos e arquitetura que pretendem melhorar a qualidade de experiência do utilizador, ao prever a quantidade de vídeo em avanço passível de ser précarregado, de forma a optimizar a eficiência de entrega das redes onde a qualidade de serviço não é possível de ser garantida. O pré-carregamento dos conteúdos é feito no servidor de cache mais próximo do cliente. Para tal, é utilizado um processo analítico hierárquico (AHP), onde através de um método subjetivo de comparação de atributos, e da aplicação de uma função de valores ponderados nas medições das métricas de qualidade de serviço, é obtida a quantidade a pré-carregar. Além deste método, é também proposta uma abordagem com técnicas de inteligência artificial. Através de redes neurais, há uma tentativa de auto-aprendizagem do comportamento das redes OTT com mais de 14.000 horas de consumo de vídeo sobre diferentes condições de qualidade, para se tentar estimar a experiência sentida e maximizar a mesma, sem degradação da entrega de serviço normal. No final, ambos os métodos propostos são avaliados num cenário de utilizadores num comboio a alta velocidade.Mestrado em Engenharia de Computadores e Telemátic

A Survey of Performance Evaluation and Control for Self-Similar Network Traffic

This paper surveys techniques for the recognition and treatment of self-similar network or internetwork traffic. Various researchers have reported traffic measurements that demonstrate considerable burstiness on a range of time scales with properties of self-similarity. Rapid technological development has widened the scope of network and Internet applications and, in turn, increased traffic volume. The exponential growth of the number of servers, as well as the number of users, causes Internet performance to be problematic as a result of the significant impact that long-range dependent traffic has on buffer requirements. Consequently, accurate and reliable measurement, analysis and control of Internet traffic are vital. The most significant techniques for performance evaluation include theoretical analysis, simulation, and empirical study based on measurement. In this research, we discuss existing and recent developments in performance evaluation and control tools used in network traffic engineering

Non-Intrusive Measurement in Packet Networks and its Applications

PhDNetwork measurementis becoming increasingly important as a meanst o assesst he performanceo f packet networks. Network performance can involve different aspects such as availability, link failure detection etc, but in this thesis, we will focus on Quality of Service (QoS). Among the metrics used to define QoS, we are particularly interested in end-to-end delay performance. Recently, the adoption of Service Level Agreements (SLA) between network operators and their customersh as becomea major driving force behind QoS measurementm: easurementi s necessaryt o produce evidence of fulfilment of the requirements specified in the SLA. Many attempts to do QoS based packet level measurement have been based on Active Measurement, in which the properties of the end-to-end path are tested by adding testing packets generated from the sending end. The main drawback of active probing is its intrusive nature which causes extraburden on the network, and has been shown to distort the measured condition of the network. The other category of network measurement is known as Passive Measurement. In contrast to Active Measurement, there are no testing packets injected into the network, therefore no intrusion is caused. The proposed applications using Passive Measurement are currently quite limited. But Passive Measurement may offer the potential for an entirely different perspective compared with Active Measurements In this thesis, the objective is to develop a measurement methodology for the end-to-end delay performance based on Passive Measurement. We assume that the nodes in a network domain are accessible.F or example, a network domain operatedb y a single network operator. The novel idea is to estimate the local per-hop delay distribution based on a hybrid approach (model and measurement-based)W. ith this approach,t he storagem easurementd ata requirement can be greatly alleviated and the overhead put in each local node can be minimized, so maintaining the fast switching operation in a local switcher or router. Per-hop delay distributions have been widely used to infer QoS at a single local node. However, the end-to-end delay distribution is more appropriate when quantifying delays across an end-to-end path. Our approach is to capture every local node's delay distribution, and then the end-to-end delay distribution can be obtained by convolving the estimated delay distributions. In this thesis, our algorithm is examined by comparing the proximity of the actual end-to-end delay distribution with the estimated one obtained by our measurement method under various conditions. e. g. in the presence of Markovian or Power-law traffic. Furthermore, the comparison between Active Measurement and our scheme is also studied. 2 Network operators may find our scheme useful when measuring the end-to-end delay performance. As stated earlier, our scheme has no intrusive effect. Furthermore, the measurement result in the local node can be re-usable to deduce other paths' end-to-end delay behaviour as long as this local node is included in the path. Thus our scheme is more scalable compared with active probing

a

www.elsevier.com/locate/comnet Service invocation admission control algorithm for multi-domain IP environment

Performance and Analysis of Transfer Control Protocol Over Voice Over Wireless Local Area Network

A thesis presented to the faculty of the College of Science and Technology at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Rajendra Patil in August of 2008

Incentive-driven QoS in peer-to-peer overlays

A well known problem in peer-to-peer overlays is that no single entity has control over the software, hardware and configuration of peers. Thus, each peer can selfishly adapt its behaviour to maximise its benefit from the overlay. This thesis is concerned with the modelling and design of incentive mechanisms for QoS-overlays: resource allocation protocols that provide strategic peers with participation incentives, while at the same time optimising the performance of the peer-to-peer distribution overlay. The contributions of this thesis are as follows. First, we present PledgeRoute, a novel contribution accounting system that can be used, along with a set of reciprocity policies, as an incentive mechanism to encourage peers to contribute resources even when users are not actively consuming overlay services. This mechanism uses a decentralised credit network, is resilient to sybil attacks, and allows peers to achieve time and space deferred contribution reciprocity. Then, we present a novel, QoS-aware resource allocation model based on Vickrey auctions that uses PledgeRoute as a substrate. It acts as an incentive mechanism by providing efficient overlay construction, while at the same time allocating increasing service quality to those peers that contribute more to the network. The model is then applied to lagsensitive chunk swarming, and some of its properties are explored for different peer delay distributions. When considering QoS overlays deployed over the best-effort Internet, the quality received by a client cannot be adjudicated completely to either its serving peer or the intervening network between them. By drawing parallels between this situation and well-known hidden action situations in microeconomics, we propose a novel scheme to ensure adherence to advertised QoS levels. We then apply it to delay-sensitive chunk distribution overlays and present the optimal contract payments required, along with a method for QoS contract enforcement through reciprocative strategies. We also present a probabilistic model for application-layer delay as a function of the prevailing network conditions. Finally, we address the incentives of managed overlays, and the prediction of their behaviour. We propose two novel models of multihoming managed overlay incentives in which overlays can freely allocate their traffic flows between different ISPs. One is obtained by optimising an overlay utility function with desired properties, while the other is designed for data-driven least-squares fitting of the cross elasticity of demand. This last model is then used to solve for ISP profit maximisation

Adaptive Quality of Service Control in Distributed Real-Time Embedded Systems

An increasing number of distributed real-time embedded systems face the critical challenge of providing Quality of Service (QoS) guarantees in open and unpredictable environments. For example, such systems often need to enforce CPU utilization bounds on multiple processors in order to avoid overload and meet end-to-end dead-lines, even when task execution times deviate significantly from their estimated values or change dynamically at run-time. This dissertation presents an adaptive QoS control framework which includes a set of control design methodologies to provide robust QoS assurance for systems at different scales. To demonstrate its effectiveness, we have applied the framework to the end-to-end CPU utilization control problem for a common class of distributed real-time embedded systems with end-to-end tasks. We formulate the utilization control problem as a constrained multi-input-multi-output control model. We then present a centralized control algorithm for small or medium size systems, and a decentralized control algorithm for large-scale systems. Both algorithms are designed systematically based on model predictive control theory to dynamically enforce desired utilizations. We also introduce novel task allocation algorithms to ensure that the system is controllable and feasible for utilization control. Furthermore, we integrate our control algorithms with fault-tolerance mechanisms as an effective way to develop robust middleware systems, which maintain both system reliability and real-time performance even when the system is in face of malicious external resource contentions and permanent processor failures. Both control analysis and extensive experiments demonstrate that our control algorithms and middleware systems can achieve robust utilization guarantees. The control framework has also been successfully applied to other distributed real-time applications such as end-to-end delay control in real-time image transmission. Our results show that adaptive QoS control middleware is a step towards self-managing, self-healing and self-tuning distributed computing platform