A multiadaptive sampling technique for cost-effective network measurements

The deployment of efficient measurement solutions to assist network management tasks without interfering with normal network operation assumes a prominent role in today’s high-speed networks attending to the huge amounts of traffic involved. From a myriad of proposals for traffic measurement, sampling techniques are particularly relevant contributing effectively for this purpose as only a subset of the overall traffic volume is handled for processing, preserving ideally the correct estimation of network statistical behavior. In this context, this paper proposes MuST – a multiadaptive sampling technique based on linear prediction, aiming at reducing significantly the measurement overhead and still assuring that traffic samples reflect the statistical characteristics of the global network traffic under analysis. Conversely to current sampling techniques, MuST is a multi and self-adaptive technique as both the sample size and interval between samples are self-adjustable parameters according to the ongoing network activity and the accuracy of prediction achieved. The tests carried out demonstrate that the proposed sampling technique is able to achieve accurate network estimations with reduced overhead, using throughput as reference parameter. The evaluation results, obtained resorting to real traffic traces representing wired and wireless aggregated traffic scenarios and actual network services, prove that the simplicity, flexibility and self-adaptability of the proposed technique can be successfully explored to improve network measurements efficiency over distinct traffic conditions. For optimization purposes, this paper also includes a study of the impact of varying the order of prediction, i.e., of considering different degrees of past memory in the self-adaptive estimation mechanism. The significance of the obtained results is demonstrated through statistical benchmarking.Fundação para a Ciência e a Tecnologia (FCT

A modular traffic sampling architecture for flexible network measurements

Dissertação de Mestrado (Programa Doutoral em Informática)The massive traffic volumes and the heterogeneity of services in today’s networks urge for flexible, yet simple measurement solutions to assist network management tasks, without impairing network performance. To turn treatable tasks requiring traffic analysis, sampling the traffic has become mandatory, triggering substantial research in the area. In fact, multiple sampling techniques have been proposed to assist network engineering tasks, each one targeting specific measurement goals and traffic scenarios. Despite that, there is still a lack of an encompassing solution able to support the flexible deployment of these techniques in production networks. In this context, this research work proposes a modular traffic sampling architecture able to foster the flexible design and deployment of efficient measurement strategies. The architecture is composed of three layers i.e., management plane, control plane and data plane covering key components to achieve versatile and lightweight measurements in diverse traffic scenarios and measurement activities. The flexibility and modularity in deploying different sampling strategies relies upon a novel taxonomy of sampling techniques, in which, current and emerging techniques are identified regarding their inner characteristics - granularity, selection trigger and selection scheme. Following the proposed taxonomy, a sampling framework prototype has been developed and used as an experimental implementation of the proposed architecture, providing a fair environment to assess and compare sampling techniques under distinct measurement scenarios. Supported by the sampling framework, distinct techniques have been evaluated regarding their performance in balancing the computational burden and the accuracy in supporting traffic workload estimation and flow analysis. The results have demonstrated the relevance and applicability of the proposed architecture, revealing that a modular and configurable approach to sampling is a step forward for improving sampling scope and efficiency.Os grandes volumes de tráfego e a heterogeneidade de serviços nas redes atuais requerem soluções de medição que sejam flexíveis e simples de modo a sustentar as tarefas de gestão de redes sem afetar o desempenho das mesmas. Para tornar tratável as tarefas que exigem análise de tráfego, tornou-se obrigatório recorrer a amostragem do tráfego, motivando uma investigação substancial na área. Como consequência, várias técnicas de amostragem foram propostas para auxiliar as tarefas de engenharia de redes, cada uma orientada a satisfazer objetivos de medição e cenários de tráfego específicos. Apesar disso, ainda não existe uma solução abrangente capaz de suportar a implantação flexível destas técnicas em redes de produção. Neste contexto, este trabalho propõe uma arquitetura modular de amostragem de tráfego capaz de fomentar a concepção flexível e a implementação de estratégias efi- cientes de medição de tráfego. A arquitetura é composta por três camadas, nomeadamente, camada de gestão, camada de controle e camada de dados, cobrindo os principais componentes para alcançar versatilidade e baixo custo computacional em variados cenários de tráfego e atividades de medição. A flexibilidade e modularidade na implementação de diferentes técnicas de amostragem baseia-se numa nova taxonomia, na qual técnicas atuais e emergentes são identificadas de acordo com suas características internas - granularidade, trigger de seleção e esquema de seleção. Seguindo a taxonomia proposta, um protótipo estruturando e agregando as diferentes técnicas de amostragem foi desenvolvido e utilizado na implementação experimental da arquitetura, permitindo avaliar e comparar as técnicas de amostragem em diversos cenários de medição. Suportado pelo protótipo desenvolvido, distintas técnicas foram avaliadas quanto ao seu desempenho em equilibrar a carga computacional e a acurácia na estimação do volume de tráfego e na análise de fluxos. Os resultados demonstraram a relevância e aplicabilidade da arquitetura de amostragem proposta, revelando que uma abordagem modular e configurável constitui um avanço no sentido de melhorar a eficiência na amostragem de tráfego

Sampling techniques applied to anomalous events detection

Dissertação de mestrado integrado em Engenharia InformáticaNowadays, one of the major worries about a network is security. Since the network has become the big platform it is, the number of attacks or attempts to steal information or just harm someone or something is getting bigger to handle or harder to find. Sampling techniques help to solve these problems as they are used to reduce the scope of the analysis, as well as the resources needed to perform it. By using sample techniques to search and find the attacks in the network traffic it will become easier to detect attacks and keep the network secure. As will be seen in the following sections, joining sampling and security is not an easy task to do. Questions such as, what are the best techniques to be used, what are the best methods to be implemented, are inevitable when using sampling. However, sampling can bring more advantages than disadvantages. Besides that, depending on the chosen measurement method, sampling technique or algorithm performed to analyse the samples, the results can change a lot according to the target for the technique. To achieve results for evaluation, a Network-based Intrusion Detection System (NIDS) will be used to identify anomalous events present in the samples.Hoje em dia, uma das maiores preocupações com uma rede é a segurança. Como a rede se tornou a grande plataforma que é, o número de ataques ou tentativas de roubar informações ou apenas prejudicar alguém ou algo está cada vez maior ou mais difícil de encontrar. As téc nicas de amostragem ajudam a resolver esses problemas visto que são utilizadas para reduzir o escopo da análise assim como os recursos necessários para realizar a mesma. Usando técnicas de amostra para procurar e localizar os ataques no tráfego da rede, facilita prevenir ataques e manter a rede segura. Como será constatado nas próximas secções, juntar amostragem e segurança não é uma tarefa fácil. Questões como, quais são as melhores técnicas a serem utilizadas, quais os melhores métodos a serem implementados, são inevitáveis aquando da utilização de amostragem. Contudo, amostragem pode trazer mais vantagens do que desvan tagens. Além disso, dependendo do método de medição escolhido, técnica de amostragem ou algoritmo usado para analisar as amostras, os resultados podem variar muito consoante o alvo da técnica. Para alcançar resultados para avaliação vai ser utilizado um Network-based Intrusion Detection System (NIDS) de forma a identificar os eventos anómalos presentes nas amostragens

Effects of channel estimation on multiuser virtual MIMO-OFDMA relay-based networks

A practical multiuser cooperative transmission scheme denoted as Virtual Maximum Ratio Transmission (VMRT) for multiple-input multiple-output-orthogonal frequency division multiple access (MIMO-OFDMA) relay-based networks is proposed and evaluated in the presence of a realistic channel estimation algorithm and using low-density parity-check (LDPC) codes. It is shown that this scheme is robust against channel estimation errors. It offers diversity and array gain, keeping the complexity low with a multiuser and multiantenna channel estimation algorithm that is simple and efficient. In addition, the combination with LDPC codes provides improved gains; diversity gains larger than 6 dB can be easily obtained with a reduced number of relays. Thus, this scheme can be used to extend coverage or increase system throughput by using simple cooperative OFDMA-based relays.The authors would like to thank Jae-Yun Ko for his valuable help at the beginning of our work. This work has been partly funded by the projects MULTIADAPTIVE (TEC2008-06327- C03-02), COMONSENS (CSD2008-00010) and CODIV (ICT-2007-215477).Publicad

A HARDWARE-SOFTWARE CO-DESIGNED WEARABLE FOR REAL-TIME PHYSIOLOGICAL DATA COLLECTION AND SIGNAL QUALITY ASSESSMENT

In the future, Smart and Connected Communities (S&CC) will use distributed wireless sensors and embedded computing platforms to produce meaningful data that can help individuals, and communities. Here, we presented a scanner, a data reliability estimation algorithm and Electrocardiogram (ECG) beat classification algorithm which contributes to the S&CC framework .In part 1, we report the design, prototyping, and functional validation of a low-power, small, and portable signal acquisition device for these sensors. The scanner was fully tested, characterized, and validated in the lab, as well as through deployment to users homes. As a test case, we show results of the scanner measuring WRAP temperature sensors with relative error within the 0.01% range. The scanner measurement shows distinguish temperature of 1F difference and excellent linear dependence between actual and measured resistance (R2 = 0.998). This device hasdemonstrated the possibility of a small, low-power portable scanner for WRAP sensors.Additionally, we explored the statistical data reliability metric (DReM) to explain the quality of bio-signal quantitatively on a scale between 0.0 -1.0. As proof of concept, we analyzed the ECG signal. Our DReM prediction algorithm measures the reliability of the ECG signals effectively with low Root mean square error = 0.010 and Mean absolute error = 0.008 and coefficient of determination R2 value of 0.990. Finally, we tested our model against the opinions of three independent judges and presented R2 value to determine the agreement between judgments vs our prediction model.We concluded our contribution to the S&CC framework by analyzing ECG beat classification with a pipeline of classifiers that focuses on improving the models performance on identifying minority classes (ventricular ectopic beat, supraventricular ectopic beat). Moreover, we intended to minimize morphological distortion introduced due to indiscriminate use of filtering techniques on ECG signals. Our approach shows an average positive predictive value 95.21%, sensitivity of95.28%, and F-1 score 95.76% respectively

Organization Global Software Development Challenges of Software Product Quality

Leaders of global software development (GSD) processes in organizations have been confronting low software product quality. Managers of these processes have faced challenges that have been affecting customer satisfaction and that have resulted in negative social impacts on public safety, business financial performance, and global economic stability. The purpose of this qualitative exploratory multiple case study was to discover a common understanding shared by managers in Canadian GSD organizations of how to meet software product quality goals and enhance customer satisfaction. The conceptual framework for the study was based on Deming\u27s 14 principles of quality management. The purposeful sample included 30 knowledgeable participants who worked in Canada as GSD managers. Semistructured interviews conducted through telephone and audioconference tools, along with the review of related documents, were used to gather data. Eight themes emerged from the data analysis: developing a clear purpose and work principles, improving processes and employee skills, developing adequate personnel management strategies, promoting autonomy and personal worker development, formulating life cycle and development techniques, identifying challenges, formulating solutions, and focusing on product quality. The research findings have implications for positively influencing social change through the provision of methods and process knowledge to GSD organizational leaders. This information consists of best management and industry practices that can be applied to achieve software product quality and customer satisfaction, create management systems, maintain a competitive advantage, and prevent global software development project failures

Classification and use of landform information to increase the accuracy of land condition monitoring in Western Australian pastoral rangelands

The aim of this research was to develop land unit scale data to assist land condition monitoring projects in pastoral rangelands in Western Australia. Landforms are a major components of land units and methods were explored to include landforms as a variable in land unit predictive modelling. Three land unit prediction models were tested, a Binary Weighted Overlay (BWO), a Fuzzy Weighted Overlay (FWO) and a Positive Weights of Evidence (PWofE) model

Multiple Adaptive System of Identification

It will be useful for students, postgraduates and doctoral research scholars who study the real objects.This scientific work aims to represent some elements of the theory of identification that are important for both practical use and further theoretical research in order to build logically complete basic and applied theory of identification as mathematically reasonable theory of knowledge of the cause-and-effect relationship in the objects of the real world. For those specialists who carry out theoretical and experimental researches (technical, economic, biological, social etc) of the real-world objects with the aim of their optimal adaptive control, diagnostics of state, forecasting the consequences and so on