Locating High-loss Links for OpenFlow Networks by Multiple Hosts to Probe Packets

We previously proposed a measurement framework for OpenFlow-based networks to promptly locate high-loss links with a small load incurred by the measurement on both the data-plane (e.g., the number of transmissions of probe packets on each link) and the control-plane (e.g., the number of accesses to switches) until locating all high-loss links. One of key components is the multicast measurement route of probe packets traversing all links in both directions. However, the previously proposed Eulerian cycle-based measurement route scheme called the backbone-and-branch tree (BBT) that uses only a single measurement host (MH) may build a too long measurement path in a large network, resulting in a low measurement accuracy and an intolerance to very high-loss, e.g., failure, links located in upstream of a measurement path. Therefore, in this paper, we newly propose an enhancement of the BBT with multiple MHs, called BBT-mMH, which can control the measurement path lengths to maintain an acceptable measurement accuracy with a small overhead on both the control-plane and data-plane. The numerical simulation demonstrates potential benefits of our proposal.23rd International Conference on Advanced Communications Technology(ICACT2021), February 07 ~ 10, 2021, Phoenix Park, Pyeongchang, Korea (South), (On-Line Conference

Statistical analysis of network traffic for anomaly detection and quality of service provisioning

Network-wide traffic analysis and monitoring in large-scale networks is a challenging and expensive task. In this thesis work we have proposed to analyze the traffic of a large-scale IP network from aggregated traffic measurements, reducing measurement overheads and simplifying implementation issues. We have provided contributions in three different networking fields related to network-wide traffic analysis and monitoring in large-scale IP networks. The first contribution regards Traffic Matrix (TM) modeling and estimation, where we have proposed new statistical models and new estimation methods to analyze the Origin-Destination (OD) flows of a large-scale TM from easily available link traffic measurements. The second contribution regards the detection and localization of volume anomalies in the TM, where we have introduced novel methods with solid optimality properties that outperform current well-known techniques for network-wide anomaly detection proposed so far in the literature. The last contribution regards the optimization of the routing configuration in large-scale IP networks, particularly when the traffic is highly variable and difficult to predict. Using the notions of Robust Routing Optimization we have proposed new approaches for Quality of Service provisioning under highly variable and uncertain traffic scenarios. In order to provide strong evidence on the relevance of our contributions, all the methods proposed in this thesis work were validated using real traffic data from different operational networks. Additionally, their performance was compared against well-known works in each field, showing outperforming results in most cases. Taking together the ensemble of developed TM models, the optimal network-wide anomaly detection and localization methods, and the routing optimization algorithms, this thesis work offers a complete solution for network operators to efficiently monitor large-scale IP networks from aggregated traffic measurements and to provide accurate QoS-based performance, even in the event of volume traffic anomalie

Towards population coding principles in the primate premotor and parietal grasping network

As humans, the only way for us to interact with the world around us is by utilizing our highly trained motor system. Therefore, understanding how the brain generates movement is essential to understanding all aspects of human behavior. Despite the importance the motor system, the manner in which the brain prepares and executes movements, especially grasping movements, is still unclear. In this thesis I undertake a number of electrophysiological and computational experiments on macaque monkeys, primates showing similar grasping behavior to humans, to shed light on how grasping movements are planned and executed across distributed brain regions in both parietal and premotor cortices. Through these experiments, I reveal how the use of large-scale electrophysiological recording of hundreds of neurons simultaneously in primates allows the investigation of network computational principles essential for grasping, and I develop a series of analytical techniques for dissecting the large data sets collected from these experiments. In chapter 2.1 I show how large-scale parallel recordings can be leveraged to make behavioral predictions on single trials. The methods used to extract single-trial predictions varied in their performance, but population-based methods provided the most consistent and meaningful interpretation of the data. In addition, the success of these behavioral predictions could be used to make inferences about how areas differ in their contribution to preparation of grasping movements. It was found that while reaction time could be predicted from the population activity of either area, performance was significantly higher using the data from premotor cortex, suggesting that population activity in premotor cortex may have a more direct effect on behavior. In chapter 2.2 I show how preparation and movement intermingle and interact with one another on the continuum between immediate and withheld movement. Our population-based and dimensionality reduction techniques enable interpretation of the data, even when single neuron tuning properties are highly temporally and functionally complex. Activity in parietal cortex stabilizes during the memory period, while it continues to evolve in premotor cortex, revealing a decodable signature of time. Furthermore, activity during movement initiation clusters into two groups, movements initiated as fast as possible and movements from memory, showing how a state shift likely occurs on the border between these two types of actions. In chapter 2.3 I show that the question of how motor cortex controls movement is an ongoing issue in the field. I address crucial details about recent methodology used to extract rotational dynamics in motor cortex. I show how a simple neural network simulation and novel statistical test reveal properties of motor cortex not examined before, showing how models of movement generation can be essential tools in adding perspective to empirical results. Finally, in chapter 2.4 I show how the specificity of hand use can be used as a tool to dissociate levels of abstraction in the visual to motor transformation in parietal and premotor cortex. While preparatory activity is mostly hand-invariant in parietal cortex, activity in premotor cortex dissociates the intended hand use well before movement. Importantly, we show how appropriate dimensionality reduction techniques can disentangle the effects of multiple task parameters and find latent dimensions consistent between areas and animals. Together, the results of my experiments reinforce the importance of seeing the motor system not as a collection of individually tuned neurons, but as a dynamic network of neurons continuously acting together to produce the complex and flexible behavior we observe in all primates

Análisis estadístico del tráfico de red para la detección de anomalías y la calidad del servicio

Network-wide traffic analysis and monitoring in large-scale networks is a challenging and expensive task. In this thesis work we have proposed to analyze the traffic of a large-scale IP network from aggregated traffic measurements, reducing measurement overheads and simplifying implementation issues. We have provided contributions in three different networking fields related to network-wide traffic analysis and monitoring in large-scale IP networks. The first contribution regards Traffic Matrix (TM) modeling and estimation, where we have proposed new statistical models and new estimation methods to analyze the Origin-Destination (OD) flows of a large-scale TM from easily available link traffic measurements. The second contribution regards the detection and localization of volume anomalies in the TM, where we have introduced novel methods with solid optimality properties that outperform current well-known techniques for network-wide anomaly detection proposed so far in the literature. The last contribution regards the optimization of the routing configuration in large-scale IP networks, particularly when the traffic is highly variable and difficult to predict. Using the notions of Robust Routing Optimization we have proposed new approaches for Quality of Service provisioning under highly variable and uncertain traffic scenarios. In order to provide strong evidence on the relevance of our contributions, all the methods proposed in this thesis work were validated using real traffic data from different operational networks. Additionally, their performance was compared against well-known works in each field, showing outperforming results in most cases. Taking together the ensemble of developed TM models, the optimal network-wide anomaly detection and localization methods, and the routing optimization algorithms, this thesis work offers a complete solution for network operators to efficiently monitor large-scale IP networks from aggregated traffic measurements and to provide accurate QoS-based performance, even in the event of volume traffic anomalies.El monitoreo y el análisis del tráfico de red en redes de gran escala es una tarea costosa y desafiante. En este trabajo de tesis nos hemos propuesto analizar el tráfico de una red IP de gran escala a partir de mediciones de tráfico agregado, reducciendo gastos de monitoreo y simplificando problemas de implementación. Hemos obtenido resultados importantes en tres áreas diferentes relacionadas con el monitoreo y el análisis del tráfico de red en redes IP a gran escala. El primer resultado concierne el modelado y la estimación de la matriz de tráfico (TM), donde hemos propuesto nuevos modelos estadísticos y nuevos métodos de estimación para analizar la flujos Origen-Destino (OD) de una TM a gran escala, a partir de mediciones de volumen en los enlaces de red, fácilmente obtenibles en los sistemas de monitoreo de red de gran escala disponibles en la actualidad. El segundo aporte corresponde con la detección y localización automática de anomalías de volumen en la TM, donde hemos introducido nuevos métodos con sólidas propiedades de optimalidad y cuyo desempeño supera el de las técnicas actualmente propuestas en la literatura para detección de anomalías de red. La última contribución considera la optimización de la configuración del enrutamiento en redes IP a gran escala, especialmente cuando el tráfico en la red es altamente variable y difícil de predecir. Utilizando las nociones de optimización robusta del enrutamiento en la red, hemos propuesto nuevos enfoques para la provisión de calidad de servicio en escenarios donde el tráfico de red es altamente variable e incierto. Con el fin de proporcionar pruebas sólidas sobre la relevancia de nuestras contribuciones, todas los métodos propuestos en este trabajo de tesis han sido evaluados y validados utilizando mediciones de tráfico real en distintas redes operativas. Al mismo tiempo, su desempeño ha sido comparado contra el obtenido por técnicas bien conocidas en cada área, mostrando mejores resultados en la mayoría de los casos. Tomando el conjunto de técnicas desarrolladas respecto del modelado de la TM, la detección y localización óptima de anomalías de red, y los algoritmos de optimización robusta del enrutamineto en la red, este trabajo de tesis ofrece una solución completa para el monitoreo eficiente de redes IP de gran escala a partir de medidas de tráfico agregado, así como también un mecanismo automático para proporcionar niveles de calidad de servicio en caso de anomalías de tráfico

Changing mentalities on flooding in the Upper Rhine valley landscape : An interdisciplinary landscape study on the role of changing flood perception on the emergence of its management in the Upper Rhine valley

This interdisciplinary project ‘Land Unter?’ aims to develop a history of flooding for the Upper Rhine valley from Strasbourg to Mannheim. A multidisciplinary variety of methods, sources, and data is used and combined into an interdisciplinary landscape study. By arranging a chronological cultural biography of the perception of flooding, mentality templates were defined. The great variety of data has been fit within these templates. The results show how the human perception changed over time, affecting the landscape of the Upper Rhine valley. The mentality templates shift from settlement on healthy and save terrace rims, to self-sufficiency for greater wealth. When flooding is seen as a sign for new disasters, the church offers protection of the property. Although, ongoing flooding and harsh climate conditions during the 10th century demanded a new approach and the necessity of actions on flood management had to be taken by the church. Draining and dike construction provided the possibility to extend lands and use former natural wilderness as a resource. While an increasing number of farmers got their own land, they were also obliged for the protection of their property. These were the results in a period when villages in the Upper Rhine valley had a hard time to survive natural vagaries. However, when techniques improved humans gradually gained total control of the river, resulting in the Tulla-rectification in the early 19th century. Only recently, a more harmonious interaction with nature seems to gain support. In order to see whether this theoretical timeline of mentalities holds up in practice, two case studies have been conducted. The first case study in Speyer did not result in prove of flood protection measures by Bishop Benno in the 11th century. However, bringing together geophysical data and historical sources provided some new prove on the course of the Rhine around Speyer during the early medieval period. Another case study in Ottersdorf near Rastatt included an archaeological excavation of a medieval dike. This dike has been dated back to the 11th century by humins and humin acids of the former surface layer. This is significantly earlier than other dikes in Western Europe and on top of it, it can be linked to the expansion of arable fields, which also seems quite early for offensive dike construction. Altogether, including the perception into a landscape study, this work provides a good link between geophysical data and cultural narratives. The case studies additionally confirmed and challenged several outcomes. Furthermore, this research has overcome the lack of knowledge on flooding in the Upper Rhine valley and additionally added to the academic debate on interdisciplinary research and inclusion of subjective landscape perception

Multi-Quality Auto-Tuning by Contract Negotiation

A characteristic challenge of software development is the management of omnipresent change. Classically, this constant change is driven by customers changing their requirements. The wish to optimally leverage available resources opens another source of change: the software systems environment. Software is tailored to specific platforms (e.g., hardware architectures) resulting in many variants of the same software optimized for different environments. If the environment changes, a different variant is to be used, i.e., the system has to reconfigure to the variant optimized for the arisen situation. The automation of such adjustments is subject to the research community of self-adaptive systems. The basic principle is a control loop, as known from control theory. The system (and environment) is continuously monitored, the collected data is analyzed and decisions for or against a reconfiguration are computed and realized. Central problems in this field, which are addressed in this thesis, are the management of interdependencies between non-functional properties of the system, the handling of multiple criteria subject to decision making and the scalability. In this thesis, a novel approach to self-adaptive software--Multi-Quality Auto-Tuning (MQuAT)--is presented, which provides design and operation principles for software systems which automatically provide the best possible utility to the user while producing the least possible cost. For this purpose, a component model has been developed, enabling the software developer to design and implement self-optimizing software systems in a model-driven way. This component model allows for the specification of the structure as well as the behavior of the system and is capable of covering the runtime state of the system. The notion of quality contracts is utilized to cover the non-functional behavior and, especially, the dependencies between non-functional properties of the system. At runtime the component model covers the runtime state of the system. This runtime model is used in combination with the contracts to generate optimization problems in different formalisms (Integer Linear Programming (ILP), Pseudo-Boolean Optimization (PBO), Ant Colony Optimization (ACO) and Multi-Objective Integer Linear Programming (MOILP)). Standard solvers are applied to derive solutions to these problems, which represent reconfiguration decisions, if the identified configuration differs from the current. Each approach is empirically evaluated in terms of its scalability showing the feasibility of all approaches, except for ACO, the superiority of ILP over PBO and the limits of all approaches: 100 component types for ILP, 30 for PBO, 10 for ACO and 30 for 2-objective MOILP. In presence of more than two objective functions the MOILP approach is shown to be infeasible