Non-stationary service curves : model and estimation method with application to cellular sleep scheduling

In today’s computer networks, short-lived flows are predominant. Consequently, transient start-up effects such as the connection establishment in cellular networks have a significant impact on the performance. Although various solutions are derived in the fields of queuing theory, available bandwidths, and network calculus, the focus is, e.g., about the mean wake-up times, estimates of the available bandwidth, which consist either out of a single value or a stationary function and steady-state solutions for backlog and delay. Contrary, the analysis during transient phases presents fundamental challenges that have only been partially solved and is therefore understood to a much lesser extent. To better comprehend systems with transient characteristics and to explain their behavior, this thesis contributes a concept of non-stationary service curves that belong to the framework of stochastic network calculus. Thereby, we derive models of sleep scheduling including time-variant performance bounds for backlog and delay. We investigate the impact of arrival rates and different duration of wake-up times, where the metrics of interest are the transient overshoot and relaxation time. We compare a time-variant and a time-invariant description of the service with an exact solution. To avoid probabilistic and maybe unpredictable effects from random services, we first choose a deterministic description of the service and present results that illustrate that only the time-variant service curve can follow the progression of the exact solution. In contrast, the time-invariant service curve remains in the worst-case value. Since in real cellular networks, it is well known that the service and sleep scheduling procedure is random, we extend the theory to the stochastic case and derive a model with a non-stationary service curve based on regenerative processes. Further, the estimation of cellular network’s capacity/ available bandwidth from measurements is an important topic that attracts research, and several works exist that obtain an estimate from measurements. Assuming a system without any knowledge about its internals, we investigate existing measurement methods such as the prevalent rate scanning and the burst response method. We find fundamental limitations to estimate the service accurately in a time-variant way, which can be explained by the non-convexity of transient services and their super-additive network processes. In order to overcome these limitations, we derive a novel two-phase probing technique. In the first step, the shape of a minimal probe is identified, which we then use to obtain an accurate estimate of the unknown service. To demonstrate the minimal probing method’s applicability, we perform a comprehensive measurement campaign in cellular networks with sleep scheduling (2G, 3G, and 4G). Here, we observe significant transient backlogs and delay overshoots that persist for long relaxation times by sending constant-bit-rate traffic, which matches the findings from our theoretical model. Contrary, the minimal probing method shows another strength: sending the minimal probe eliminates the transient overshoots and relaxation times

Large-scale secondary circulations in a limited area model – the impact of lateral boundaries and resolution

Within their domain, regional climate and weather forecasting models deviate from the driving data. Small-scale deviations are a desired effect of adding regional details. There are, however, also deviations of the large-scale circulation, which can be caused by orographic effects and depend on the large-scale flow condition. These ‘secondary circulations’ (SCs) are confined to the model domain due to the prescribed boundary conditions. Here, the impact of different regional model configurations on the SC is analysed in a case study for the European region using an ensemble approach. It is shown that at 500 hPa, vortices of the SC have diameters on the order of several thousand kilometres and are related to wind speed anomalies of more than 5 m/s and geopotential height anomalies of more than 5 dam. The spatial structure and the amplitude of the SC strongly depend on the location of the lateral boundaries. The impact of the boundary location on the anomalies is on the same order of magnitude as the anomalies themselves. The resolution of the regional model, as well as the application of spectral nudging and a smoothed topography, affects mainly the amplitude of the SC, but not the spatial structure

Weather impacts on various types of road crashes: a quantitative analysis using generalized additive models

Adverse weather conditions can have different effects on different types of road crashes. We quantify the combined effects of traffic volume and meteorological parameters on hourly probabilities of 78 different crash types using generalized additive models. Using tensor product bases, we model non-linear relationships and combined effects of different meteorological parameters. We evaluate the increase in relative risk of different crash types in case of precipitation, sun glare and high wind speeds. The largest effect of snow is found in case of single-truck crashes, while rain has a larger effect on single-car crashes. Sun glare increases the probability of multi-car crashes, in particular at higher speed limits and in case of rear-end crashes. High wind speeds increase the probability of single-truck crashes and, for all vehicle types, the risk of crashes with objects blown on the road. A comparison of the predictive power of models with and without meteorological variables shows an improvement of scores of up to 24%, which makes the models suitable for applications in real-time traffic management or impact-based warning systems. These could be used by authorities to issue weather-dependent driving restrictions or situation-specific on-board warnings to improve road safety

Modeling hourly weather-related road traffic variations for different vehicle types in Germany

Weather has a substantial influence on people’s travel behavior. In this study we analyze if meteorological variables can improve predictions of hourly traffic counts at 1400 stations on federal roads and highways in Germany. Motorbikes, cars, vans and trucks are distinguished. It is evaluated in how far the mean squared error of Poisson regression models for hourly traffic counts is reduced by using precipitation, temperature, cloud cover and wind speed data. It is shown that in particular motorbike counts are strongly weather-dependent. On federal roads the mean squared error is reduced by up to 60% in models with meteorological predictor variables, when compared to models without meteorological variables. A detailed analysis of the models for motorbike counts reveals non-linear relationships between the meteorological variables and motorbike counts. Car counts are shown to be specifically sensitive to weather in touristic regions like seaside resorts and nature parks. The findings allow for several potential applications like improvements of route planning in navigation systems, implementations in traffic management systems, day-ahead planning of visitor numbers in touristic areas or the usage in road crash modelling

Towards a Usability Measurement Framework for Process Modelling Tools

Usability is widely used in software engineering as a criterion of product quality and acceptance. Studies revealed that one dollar investment in usability brings 10 times more profit than investments in advertising. Usability of websites and desktop applications has been extensively investigated during the last decades. However, little was done in the area of usability of process modelling tools. We argue that generic usability measurement frameworks do not reflect the particularities of this type of applications. In our research-in-progress paper we have conducted a number of empirical studies in order to determine environment dimensions and their effect on usability attributes. As a result we propose a theoretical usability measurement framework whose underlying hypotheses will be evaluated in future research

Reinforcement Learning for Wind Turbine Load Control: How AI can drive tomorrow‘s wind turbines

Load control strategies for wind turbines are used to reduce the structural wear of the turbine without reducing energy yield. Until now, these control strategies are almost exclusively built up-on linear approaches like PID and model-based controllers due to their robustness. However, advances in turbine size and capabilities create a need for more complex control strategies that can effectively address design challenges in modern turbines. This work presents WINDL, a load control policy based on a neural network, which is trained through model-free Reinforcement Learning (RL) on a simulated wind turbine. While RL has achieved great success in the past on games and simple simulation benchmarks, applications to more complex control problems are starting to emerge just recently. We show that through the usage of regularization techniques and signal transformations, such an application to the field of wind turbine load control is possible. Using a smoothness regularizer, we incentivize the highly non-linear neural network policy to output control actions that are safe to apply to a wind turbine. The Coleman transformation, a common tool for the design of traditional PID-based load control strategies, is used to project signals into a stationary coordinate space, increasing robustness and final policy performance. Trained to control a large offshore turbine in a model-free fashion, WINDL finds a control policy that outperforms a state-of-the-art controller based on the IPC strategy with respect to the prima-ry optimization goal blade loads. Using the DEL metric, we measure 54.1% lower blade loads in the steady wind and 13.45% lower blade loads in the turbulent wind scenario. While such levels of blade reduction come with slightly worse performance on secondary optimi-zation goals like pitch wear and power production, we demonstrate the ability to control the trade-off between different optimization goals on the example of pitch versus blade loads. To comple-ment our findings, we perform a qualitative analysis of the policy behavior and learning process. We believe our work to be the first application of RL to wind turbine load control that exceeds baseline performance in the primary optimization metric, opening up the possibility of including specialized load controllers for targeting critical design driving scenarios of modern large wind turbines.:Problem Method Aim Results Conclusio

Towards Usability Guidelines for Mobile Websites and Applications

The market for mobile devices is growing rapidly nowadays. Constant technolog-ical improvements provide great opportunities for the creation of mobile applica-tions. For the success of a mobile application or website, one of the main con-cerns, besides security issues, is usability. Poor usability decreases user produc-tivity and consequently causes loss of users. In order to avoid these problems, usability aspects have to be considered already during the design phase of the ap-plication, e.g. by following predefined usability guidelines. Although usability guidelines for web development are already in place since the 1990s, structured and evaluated usability guidelines for mobile applications can rarely be found in scientific literature. Thus, in this paper we introduce a catalogue of usability guidelines for mobile applications and websites, and subsequently demonstrate their usage by applying them in two case studies: the development of a mobile application and a mobile website

Forced migration and human capital : evidence from post- WWII population transfers

We exploit a unique historical setting to study the long-run effects of forced migration on investment in education. After World War II, the Polish borders were redrawn, resulting in large-scale migration. Poles were forced to move from the Kresy territories in the East (taken over by the USSR) and were resettled mostly to the newly acquired Western Territories, from which Germans were expelled. We combine historical censuses with newly collected survey data to show that, while there were no pre-WWII differences in education, Poles with a family history of forced migration are significantly more educated today. Descendants of forced migrants have on average one extra year of schooling, driven by a higher propensity to finish secondary or higher education. This result holds when we restrict ancestral locations to a subsample around the former Kresy border and include fixed effects for the destination of migrants. As Kresy migrants were of the same ethnicity and religion as other Poles, we bypass confounding factors of other cases of forced migration. We show that labor market competition with natives and selection of migrants are also unlikely to drive our results. Survey evidence suggests that forced migration led to a shift in preferences, away from material possessions and towards investment in a mobile asset – human capital. The effects persist over three generations