Rosa Maria Meri Leão

Quality assessment of interactive voice applications

The conversational quality of a VoIP communication is dependent on several factors such as the coding process used, the network conditions and the type of error correction or concealment employed. Furthermore, the quality perceived by the users is also dependent on the characteristics of the conversation itself. Assessing this kind of communication is a very difficult problem, and most of the studies available in the literature simplify the issue by restricting the analysis to only one or two parameters. However, the number of potentially affecting factors is typically higher, and their joint effect on quality is complex. In this paper we study the combined effects of bit rate, forward error correction, loss rate, loss distribution, delay and jitter on the perceived conversational quality. In order to achieve this we use the pseudo-subjective quality assessment (PSQA) technique, which allows us to obtain accurate, subjective-like assessments, in real time if necessary. Our contributions are thus twofold: firstly, we offer a detailed analysis of the impact of these parameters and their interactions on the perceived conversational quality. Secondly, we show how the PSQA methodology can be used to provide accurate conversational quality estimations.

Estimating self-sustainability in peer-to-peer swarming systems

Peer-to-peer swarming is one of the \emph{de facto} solutions for distributed content dissemination in todays Internet. By leveraging resources provided by clients, swarming systems reduce the load on and costs to publishers. However, there is a limit to how much cost savings can be gained from swarming; for example, for unpopular content peers will always depend on the publisher in order to complete their downloads. In this paper, we investigate this dependence. For this purpose, we propose a new metric, namely \emph{swarm self-sustainability}. A swarm is referred to as self-sustaining if all its blocks are collectively held by peers; the self-sustainability of a swarm is the fraction of time in which the swarm is self-sustaining. We pose the following question: how does the self-sustainability of a swarm vary as a function of content popularity, the service capacity of the users, and the size of the file? We present a model to answer the posed question. We then propose efficient solution methods to compute self-sustainability. The accuracy of our estimates is validated against simulation. Finally, we also provide closed-form expressions for the fraction of time that a given number of blocks is collectively held by peers.

Modeling Resource Sharing Dynamics of VoIP Users over a WLAN Using a Game-Theoretic Approach

We consider a scenario in which users share an access point and are mainly interested in VoIP applications. Each user is allowed to adapt to varying network conditions by choosing the transmission rate at which VoIP traffic is received. We denote this adaptation process by end-user congestion control, our object of study. The two questions that we ask are: (1) what are the performance consequences of letting the users to freely choose their rates? and (2) how to explain the adaptation process of the users? We set a controlled lab experiment having students as subject to answer the first question, and we extend an evolutionary game-theoretic model to address the second. Our partial answers are the following: (1) free users with local information can reach an equilibrium which is close to optimal from the system perspective. However, the equilibrium can be unfair; (2) the adaptation of the users can be explained using a game theoretic model. We propose a methodology to parameterize the latter, which involves active network measurements, simulations and an artificial neural network to estimate the QoS perceived by the users in each of the states of the model.

The TANGRAM-II Environment

TANGRAM-II is an environment for computer and communication system modelingand experimentation, developed for research and educational purposes. It provides a general user interface based on an object oriented paradigm and a variety of solvers to obtain the measures of interest. The environment also includes modules useful for computer network experimentation, and multimedia tools to aid in the modelingpro cess and collaborative work.

The TANGRAMII integrated modeling environment for computer systems and networks

The TANGRAM-II tool has been developed aiming at supporting the performance analyst throughout the entire modeling process, from model construction and model solution to experimentation. The tool has a powerful user interface that can be tailored to specific problem domain, it includes a rich set of analytic solution techniques, distinct options for obtaining the measures of interest, a hybrid fluid and event driven simulator, visualization features to follow the models evolution with time, traffic generators and active measurement techniques to assist the user in performing computer networking experimentation. These and additional characteristics make TANGRAM-II a unique tool for research and education.

Survivability models for the assessment of smart grid distribution automation network designs

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams through different lenses, smart grids require a unified and holistic approach that takes into consideration the interplay of communication reliability, energy backup, distribution automation topology, energy storage and intelligent features such as automated failure detection, isolation and restoration (FDIR) and demand response. In this paper, we present an analytical model and metrics for the survivability assessment of the distribution power grid network. The proposed metrics extend the system average interruption duration index (SAIDI), accounting for the fact that after a failure the energy demand and supply will vary over time during a multi-step recovery process. The analytical model used to compute the proposed metrics is built on top of three design principles: state space factorization, state aggregation and initial state conditioning. Using these principles, we reduce a Markov chain model with large state space cardinality to a set of much simpler models that are amenable to analytical treatment and efficient numerical solution. In the special case where demand response is not integrated with FDIR, we provide closed form solutions to the metrics of interest, such as the mean time to repair a given set of sections. We have evaluated the presented model using data from a real power distribution grid and we have found that survivability of distribution power grids can be improved by the integration of the demand response feature with automated FDIR approaches. Our empirical results indicate the importance of quantifying survivability to support investment decisions at different parts of the power grid distribution network.

Survivability analysis of power distribution in smart grids with active and reactive power modeling

A paradigm shift is taking place in the realm of power distribution networks. Power distribution networks that have been traditionally built to meet peak demand are now being automated to offer reliability on demand, i.e., smart distribution power grids can be automatically reconfigured after events such as power failures. In future distribution automation networks an important design decision will consist of which approach to use to avoid voltage drops. A standard approach is to add static capacitors to the distribution circuit. Novel techniques include the automatic reduction of active or reactive load through demand response, or the addition of distributed generators that can tradeoff active load for reactive load. In this paper, we introduce a new modeling approach to assist in such design decisions. The survivability of a system is its ability to function during and after a failure. In survivability analysis, the initial state of the system is set to a failure state, so survivability is “conditional performability” [9, 11]. The main contribution of this paper is the development of a model to study the power distribution in smart grids during the (transient) period that starts after a failure till the system fully recovers. The proposed model bridges power flow modeling of reactive power compensation [8, 14] with performability/survivability modeling of automation distribution networks [1]. We use a Markov chain to characterize the phased recovery of the system after a failure [5]. Then, we associate to each state of the Markov chain a set of corresponding rewards to characterize the active and reactive power supplied and demanded in that state.

Modeling, analysis, measurement and experimentation with the Tangram-II integrated environment

A large number of performance evaluation tools have been developed over the years to support the analyst in the difficult task of model building. As systems increase in complexity, the need is critical for tools that are able to help the user throughout the whole modeling cycle, from model building to model solution and experimentation. In this work we describe the main features of the TANGRAM-II modeling environment. The tool has a powerful and flexible model interface, unique algorithms for the numerical solution of models, includes an event driven and fluid simulators that provides a variety of facilities useful for obtaining the measures of interest, and has a traffic engineering environment integrated with the other tool modules.

Implications of peer selection strategies by publishers on the performance of P2P swarming systems

In peer-to-peer swarming systems, as peers join a swarm to download a content they bring resources such as bandwidth and memory to the system. That way, the capacity of the system increases with the arrival rate of peers. Furthermore, if publishers are intermittent, increasing the arrival rate of peers can increase content availability [7]. In the presence of stable publishers that have enough service capacity for peers to smoothly complete their download [6], increasing the arrival rate of peers decreases the probability that a piece will be unavailable among peers. However, if the capacity of the stable publisher, U pieces/second, is not large enough, it has been shown that the system might be unstable [3, 5, 14]. Hajek and Zhou [3, 14], following up work by Mathieu and Reynier [5], have shown that if the arrival rate of peers, λ, is greater than U , the number of peers increases unboundedly with time. It has also been shown that simple strategies can alleviate, and in some cases resolve, the instability problem. For instance, if peers reside in the system after completing their downloads, on average, the same time that they take to download a piece, then the system is always stable [14]. Nevertheless, as peers have no incentive to stay in the system after completing their downloads, it is important to investigate whether other simple strategies that do not depend on providing incentives for peers to remain online after the download completion can improve system performance and stability. In a peer to peer system, each peer has to make two decisions before transmitting each piece: 1) which piece to transmit and 2) to whom to transmit it. Although the former question has received some attention in previous works (for instance, it has been shown that rarest-first piece selection and random useful piece selection yield the same stability region [3]), to the best of our knowledge the implications of the peer selection strategy have not been discussed yet (previous works assumed random peer selection [3, 9, 14], a notable exception being [5] – see related work section). Let the throughput be the rate at which peers leave the system. The goal of this paper is to evaluate the impact of different peer selection strategies on the throughput (hence, stability) of the system. We pose the following questions: a) how to increase the throughput of the system by letting peers strategically select their neighbors? b) how does throughput scale with the number of peers in a closed peer-to-peer swarming system? We provide the following answers to the above questions. First, we derive an upper bound on the throughput when the stable publisher adopts the most deprived peer selection [1] and rarest-first piece selection, while peers adopt random peer selection and random useful piece selection. The bound is significantly larger than the maximum attainable throughput when both peers and publishers adopt random peer and random useful piece selection. Then, we consider a closed system and we use a simple Markov chain model to study how the throughput of the system scales with the number of peers.

A Set of Tools for Traffic Modeling, Analysis and Experimentation

Traffic characterization and modeling has been an extensive area of research in the last few years. Many of these studies aim at constructing accurate models to predict the network performance. Performance studies includes: analysis of admission control algorithms, buffer dimensioning, and many others. Several steps are needed to conduct a performance study. First, it is necessary to characterize the traffic generated by the applications, second it is important to choose an appropriate model to represent this traffic. The analysis of the accuracy of a traffic model is, in general, based on the match of some descriptors and on how well it predicts the performance measures. Finally, the user would like to construct and solve a network performance model. A large number of models have been proposed in the literature to describe a variety of traffic generated by data, audio and video sources. The model which is the more accurate for each type of traffic is still an open issue in the literature, and thus it is important to provide an environment to aid the user in the development and analysis of traffic models. The focus of this study is two-fold: to obtain analytical expressions for some important traffic descriptors calculated from general Markovian models and to present a set of modules we have implemented to provide an environment useful for traffic modeling, analysis and experimentation. These modules are currently being integrated in the TANGRAM-II modeling tool.