High Speed Lines (HSL) in rail passenger services are regarded as one of the most significant projects in many countries comparing to other projects in the transportation area. According to the EU (European Council Directive 96/48/EC,2004) , high-speed lines are either new-built lines for speeds of 250km/h or greater, or in some cases upgraded traditional lines. At the beginning of 2008, there were 10,000 km of new HSL lines in operation, and by taking into account the upgraded conventional lines, in total, there were 20,000 km line in the world. The network is growing fast because of the demand for short travelling time and comfort isincreasing rapidly. Since HSL projects require a lot of capital, it is getting more important for governments and companies to estimate and to calculate the total costs and benefits of building, maintaining, and operating of HSL so that they can decide better and more reliable in choosing between projects. There are many parameters which affect the total costs and benefits of an HSL. The most important parameter is dimensional speed which has a great influence on other parameters. For example, tunnels need larger cross section for higher speed which increases construction costs. More important, higher speed also influences the number of passengers attracted from other modes of transport. Due to a large number of speed-dependant parameters, it is not a simple task to estimate an optimal dimensional speed by calculating the costs and benefits of an HSL manually. It is also difficult to do analysis for different speeds, as speed changes many other relevant parameters. As a matter of fact, there is a need for a computational model to calculate the cost-benefit for different speeds. Based on the computational model, it is possible to define different scenarios and compare them to each other to see what the potentially optimal speed would be for a new HSL project. Besides the optimal speed, it is also possible to analyze and find effects of two other important parameters, fare and frequency, by cost-benefit analysis (CBA). The probability model used in the calculation is based on an elasticity model, and input parameters are subject to flexibility to calibrate the model appropriately. Optimal high-speed line (OHSL) tool is developed to make the model accessible for the users.

Le principe d'élasticité est d'assurer que juste les ressources nécessaires sont provisionnées pour préserver le bon fonctionnement des services Cloud. La propriété d'élasticité permet d'éviter la sous-utilisation et la sur-utilisation des ressources. La propriété d'élasticité a attiré beaucoup d'attention ces dernières années comme une tâche pivot qui permet d'assurer un bon compromis entre les QdS désirées et les coûts opérationnels des AbSs. Toutefois, le contrôle d'élasticité des AbSs et la définition des stratégies d'élasticité non-triviales sont encore des tâches difficiles à réaliser. Une stratégie d'élasticité est utilisée pour gérer l'élasticité en décidant des trois éléments essentiels: 'quand', 'où' et 'comment' utiliser les mécanismes d'élasticité (par exemple, les opérations de duplication/consolidation de services) qui permettent d'assurer les objectifs de QdS avec une consommation optimisée des ressources. La complexité de définition de stratégies d'élasticité augmente avec les métriques de QdS considérées. La difficulté de cette tâche est de plus accentuée avec l'absence d'un langage unifiée pour exprimer ces stratégies. Notre travail de thèse vise à remédier aux limites des approches existantes pour la gestion des stratégies d'élasticité. Il consiste à développer un langage pour décrire différents types des stratégies d'élasticité d'une façon unifiée. Nous définissons un modèle formel qui cadre l'ensemble de métriques à considérer, définit les opérations d'élasticité à appliquer et spécifie les lois d'émission de requêtes. Ce modèle servira aussi pour appliquer et valider les stratégies spécifiées. Nous travaillons en plus sur l'alignement des contrats de qualités de services (Service Level Agreement) avec les stratégies d'élasticité. / Elasticity is the ability of a system to be adjustable to workload change by allocating and releasing as many resources as needed while ensuring the agreed QoS. It has played a pivotal role in many research works for ensuring QoS. Therefore, Elasticity management is witnessing a lot of attention from IT community as a pivotal issue for finding the right tradeoffs between QoS levels and operational costs by working on developing novel methods and mechanisms. However, controlling business process elasticity and defining non-trivial elasticity strategies are challenging issues. Elasticity strategies are policies that are used to manage elasticity by deciding when, where and how to use elasticity mechanisms (e.g, adding or removing resources). Many strategies can be defined to ensure applications elasticity. The abundance of possible strategies requires their evaluation and validation in order to guarantee their effectiveness before using them in real Cloud environments. Our thesis work aims to overcome the limitations of the existing approaches for elasticity strategies management. It consists in developing a configurable Domain-Specific language to describe different types of elasticity strategies in a unified way. We define a formal model that captures a set of QoS metrics and defines elasticity operations. This model will also be used to define and verify elasticity strategies. We will also work on the alignment of Service Level Agreements with the elasticity strategies.

An efficient application and development of renewable energy sources is one of the most important contribution to the energetic balance of the human society. Anyhow, statistical model of the renewable energy market, which would fundamentally explain relevant economical rules related to these perspective energetic resources, is not clearly known up to now. Nevertheless, the relevant statistical data concerning application of solar energy (photovoltaic and thermo-solar heating) are available for the last twenty years. Based on the economic models, statistical data concerning sales of photovoltaic models and thermo-solar collectors sales have been analysed in this work. It has been shown that the model of constant elasticity predicts an exponential increase which will slow down when a certain level of annual cumulative sales was reached. The model of constant elasticity was found to be successful to interpret past sales data. In the approach of variable elasticity model the parameter of the elasticity has been modified as a function of variables such as market volume, price and time through the statistical evaluation. It enabled to calculate initial, saturation and competitive market conditions, as well. Whereas the constant elasticity demand model describes exponential growth of sales and installations, which was characteristic for the beginning of the application of these renewable resources of energy, the variable elasticity demand model describes a more realistic situation, where cumulative sales either increase or decrease and prices vary subsequently. Simple growth model of unlimited demand based on the growing sales is not realistic and could not be feasible in the long term. The market elasticity could be understood as a real economical parameter representing percentual market increase or decrease at a given time; in the variable demand elasticity model, the constant elasticity is replaced by a function of a market volume, price and time. In this case, we can estimate model parameters for the different market conditions: growth, saturation and decrease. The function representing the capital adequacy in the generalized market model has also been deliberated. Statistical models have been used to determine cumulative sales and market prices of photovoltaic modules and thermo-solar collectors. Moreover, model parameters have been used for the calculation of the realized photovoltaic and thermo solar projects' capital adequacy on the renewable energy market. By using model parameters, renewable energy market forecast up to 2020 has been estimated. We have used generalized market model to credibly estimate future renewable energy market until 2020; as well as extend model parameterization on other resources of renewable energy (water and wind, geothermal sources, biomass) and set prices of energy produced from these renewable sources. Potential energetic savings have been estimated for households (apartments and private houses), who can be relevant consumers of energy from renewable sources. We have performed statistical findings on randomly selected files, where we have reached a real energy consumption, to prove this. This research allowed us to perform a real estimate of a renewable energy contribution to the total energy balance. We have successfully proved that linearly growing capital adequacy function, with an annual growth between 2.5% and 3.0%, is reflecting the renewable energy market sufficiently and is fully in line with an average growth of the total energy consumption. Renewable energy share on the total energy balance will grow substantially to reach a level of 15% in 2015 on the world market and a level of 8% in the Czech Republic for the same period with a perspective to reach a level of 11% in 2020 respectively. Assuming this level of renewable energy on the total production will lead to a decrease of CO2 emissions by three million of tones in 2015 and by four million of tones in 2020. Final reach of this status quo is fully predicted by our statistical model for renewable energy market.