Data of the molecular dynamics simulations of mutations in the human connexin46 docking interface

The structure of hCx26 derived from the X-ray analysis was used to generate a homology model for hCx46. Interacting connexin molecules were used as starting model for the molecular dynamics (MD) simulation using NAMD and allowed us to predict the dynamic behavior of hCx46wt and the cataract related mutant hCx46N188T as well as two artificial mutants hCx46N188Q and hCx46N188D. Within the 50 ns simulation time the docked complex composed of the mutants dissociate while hCx46wt remains stable. The data indicates that one hCx46 molecule forms 5-7 hydrogen bonds (HBs) with the counterpart connexin of the opposing connexon. These HBs appear essential for a stable docking of the connexons as shown by the simulation of an entire gap junction channel and were lost for all the tested mutants. The data described here are related to the research article entitled "The cataract related mutation N188T in human connexin46 (hCx46) revealed a critical role for residue N188 in the docking process of gap junction channels" (Schadzek et al., 2015) [1].

Simulating the effects of vegetation and landscape structure on fire behavior in northeastern Portugal: the case of holm oak (Quercus rotundifolia)

O fogo é um processo frequente nas paisagens do norte de Portugal. Estudos anteriores mostraram que os bosques de azinheira (Quercus rotundifolia) persistem após a passagem do fogo e ajudam a diminuir a sua intensidade e taxa de propagação. Os principais objetivos deste estudo foram compreender e modelar o efeito dos bosques de azinheira no comportamento do fogo ao nível da paisagem da bacia superior do rio Sabor, localizado no nordeste de Portugal. O impacto dos bosques de azinheira no comportamento do fogo foi testado em termos de área e configuração de acordo com cenários que simulam a possível distribuição destas unidades de vegetação na paisagem, considerando uma percentagem de ocupação da azinheira de 2.2% (Low), 18.1% (Moderate), 26.0% (High), e 39.8% (Rivers). Estes cenários tiveram como principal objetivo testar 1) o papel dos bosques de azinheira no comportamento do fogo e 2) de que forma a configuração das manchas de azinheira podem ajudar a diminuir a intensidade da linha de fogo e área ardida. Na modelação do comportamento do fogo foi usado o modelo FlamMap para simular a intensidade de linha do fogo e taxa de propagação do fogo com base em modelos de combustível associados a cada ocupação e uso do solo presente na área de estudo, e também com base em fatores topográficos (altitude, declive e orientação da encosta) e climáticos (humidade e velocidade do vento). Foram ainda usados dois modelos de combustível para a ocupação de azinheira (áreas interiores e de bordadura), desenvolvidos com base em dados reais obtidos na região. Usou-se o software FRAGSATS para a análise dos padrões espaciais das classes de intensidade de linha do fogo, usando-se as métricas Class Area (CA), Number of Patches (NP) e Large Patches Index (LPI). Os resultados obtidos indicaram que a intensidade da linha de fogo e a taxa de propagação do fogo variou entre cenários e entre modelos de combustível para o azinhal. A intensidade média da linha de fogo e a taxa média de propagação do fogo decresceu à medida que a percentagem de área de bosques de azinheira aumentou na paisagem. Também foi observado que as métricas CA, NP e LPI variaram entre cenários e modelos de combustível para o azinhal, decrescendo quando a percentagem de área de bosques de azinheira aumentou. Este estudo permitiu concluir que a variação da percentagem de ocupação e configuração espacial dos bosques de azinheira influenciam o comportamento do fogo, reduzindo, em termos médios, a intensidade da linha de fogo e a taxa de propagação, sugerindo que os bosques de azinhal podem ser usados como medidas silvícolas preventivas para diminuir o risco de incêndio nesta região.

Assessing the activity coefficients of water in cholinium-based ionic liquids: experimental measurements and COSMO-RS modeling

The vapor liquid-equilibrium of water + ionic liquids is relevant for a wide range of applications of these compounds. It is usually measured by ebulliometric techniques, but these are time consuming and expensive. In this work it is shown that the activity coefficients of water in a series of cholinium-based ionic liquids can be reliably and quickly estimated at 298.15K using a humidity meter instrument. The cholinium based ionic liquids were chosen to test this experimental methodology since data for water activities of quaternary ammonium salts are available in the literature allowing the validation of the proposed technique. The COSMO-RS method provides a reliable description of the data and was also used to understand the molecular interactions occurring on these binary systems. The estimated excess enthalpies indicate that hydrogen bonding between water and ionic liquid anion is the dominant interaction that governs the behavior of water and cholinium-based ionic liquids systems, while the electrostatic-misfit and van der Walls forces have a minor contribution to the total excess enthalpies.

The antecedents of post-initial adoption behavior in a S-D logic context

Repercussions of innovation adoption and diffusion studies have long been imperative to the success of novel introductions. However, perceptions and deductions of current innovation understandings have been changing over time. The paradigm shift from the goods-dominant (G-D) logic to the service-dominant (S-D) logic potentially makes the distinction between product (goods) innovation and service innovation redundant as the S-D logic lens views all innovations as service innovations (Vargo and Lusch, 2004; 2008; Lusch and Nambisan, 2015). From this perspective, product innovations are in essence service innovations, as goods serve as mere distribution mechanisms to deliver service. Nonetheless, the transition to such a broadened and transcending view of service innovation necessitates concurrently a change in the underlying models used to investigate innovation and its subsequent adoption. The present research addresses this gap by engendering a novel model for the most crucial period of service diffusion within the S-D logic context – the post-initial adoption phase, which demarcates an individual’s behavior after the initial adoption decision of a service. As a wellfounded understanding of service diffusion and the complementary innovation adoption still lingers in its infancy, the current study develops a model based on interdisciplinary domains mapping. Here fore, knowledge of the relatively established viral source domain is mapped to the comparatively undetermined target domain of service innovation adoption. To assess the model and test the importance of the explanatory variables, survey data from 750 respondents of a bank in Northern Germany is scrutinized by means of Structural Equation Modeling (SEM). The findings reveal that the continuance intention of a customer, actual usage of the service and the customer influencer value all constitute important postinitial adoption behavior that have meaningful implications for a successful service adoption. Second, the four constructs customer influencer value, organizational commitment, perceived usefulness and service customization are evidenced to have a differential impact on a iv customer’s post-initial adoption behavior. Third, this study indicates that post-initial adoption behavior further underlies the influence of a user’s age and besides that is also provoked by the internal and external environments of service adoption. Finally, this research amalgamates the broad view of service innovation by Nambisan and Lusch (2015) with the findings ensuing this enquiry’s model to arrive at a framework that it both, generalizable and practically applicable. Implications for academia and practitioners are captured along with avenues for future research.

A novel methodology for simulating contact-line behavior in capillary-driven flows

Despite the wide swath of applications where multiphase fluid contact lines exist, there is still no consensus on an accurate and general simulation methodology. Most prior numerical work has imposed one of the many dynamic contact-angle theories at solid walls. Such approaches are inherently limited by the theory accuracy. In fact, when inertial effects are important, the contact angle may be history dependent and, thus, any single mathematical function is inappropriate. Given these limitations, the present work has two primary goals: 1) create a numerical framework that allows the contact angle to evolve naturally with appropriate contact-line physics and 2) develop equations and numerical methods such that contact-line simulations may be performed on coarse computational meshes.

Fluid flows affected by contact lines are dominated by capillary stresses and require accurate curvature calculations. The level set method was chosen to track the fluid interfaces because it is easy to calculate interface curvature accurately. Unfortunately, the level set reinitialization suffers from an ill-posed mathematical problem at contact lines: a ``blind spot'' exists. Standard techniques to handle this deficiency are shown to introduce parasitic velocity currents that artificially deform freely floating (non-prescribed) contact angles. As an alternative, a new relaxation equation reinitialization is proposed to remove these spurious velocity currents and its concept is further explored with level-set extension velocities.

To capture contact-line physics, two classical boundary conditions, the Navier-slip velocity boundary condition and a fixed contact angle, are implemented in direct numerical simulations (DNS). DNS are found to converge only if the slip length is well resolved by the computational mesh. Unfortunately, since the slip length is often very small compared to fluid structures, these simulations are not computationally feasible for large systems. To address the second goal, a new methodology is proposed which relies on the volumetric-filtered Navier-Stokes equations. Two unclosed terms, an average curvature and a viscous shear VS, are proposed to represent the missing microscale physics on a coarse mesh.

All of these components are then combined into a single framework and tested for a water droplet impacting a partially-wetting substrate. Very good agreement is found for the evolution of the contact diameter in time between the experimental measurements and the numerical simulation. Such comparison would not be possible with prior methods, since the Reynolds number Re and capillary number Ca are large. Furthermore, the experimentally approximated slip length ratio is well outside of the range currently achievable by DNS. This framework is a promising first step towards simulating complex physics in capillary-dominated flows at a reasonable computational expense.

Guided Migration and Collective Behavior: Cell Dynamics during Cancer Progression

This dissertation focuses on gaining understanding of cell migration and collective behavior through a combination of experiment, analysis, and modeling techniques. Cell migration is a ubiquitous process that plays an important role during embryonic development and wound healing as well as in diseases like cancer, which is a particular focus of this work. As cancer cells become increasingly malignant, they acquire the ability to migrate away from the primary tumor and spread throughout the body to form metastatic tumors. During this process, changes in gene expression and the surrounding tumor environment can lead to changes in cell migration characteristics. In this thesis, I analyze how cells are guided by the texture of their environment and how cells cooperate with their neighbors to move collectively. The emergent properties of collectively moving groups are a particular focus of this work as collective cell dynamics are known to change in diseases such as cancer. The internal machinery for cell migration involves polymerization of the actin cytoskeleton to create protrusions that---in coordination with retraction of the rear of the cell---lead to cell motion. This actin machinery has been previously shown to respond to the topography of the surrounding surface, leading to guided migration of amoeboid cells. Here we show that epithelial cells on nanoscale ridge structures also show changes in the morphology of their cytoskeletons; actin is found to align with the ridge structures. The migration of the cells is also guided preferentially along the ridge length. These ridge structures are on length scales similar to those found in tumor microenvironments and as such provide a system for studying the response of the cells' internal migration machinery to physiologically relevant topographical cues. In addition to sensing surface topography, individual cells can also be influenced by the pushing and pulling of neighboring cells. The emergent properties of collectively migrating cells show interesting dynamics and are relevant for cancer progression, but have been less studied than the motion of individual cells. We use Particle Image Velocimetry (PIV) to extract the motion of a collectively migrating cell sheet from time lapse images. The resulting flow fields allow us to analyze collective behavior over multiple length and time scales. To analyze the connection between individual cell properties and collective migration behavior, we compare experimental flow fields with the migration of simulated cell groups. Our collective migration metrics allow for a quantitative comparison between experimental and simulated results. This comparison shows that tissue-scale decreases in collective behavior can result from changes in individual cell activity without the need to postulate the existence of subpopulations of leader cells or global gradients. In addition to tissue-scale trends in collective behavior, the migration of cell groups includes localized dynamic features such as cell rearrangements. An individual cell may smoothly follow the motion of its neighbors (affine motion) or move in a more individualistic manner (non-affine motion). By decomposing individual motion into both affine and non-affine components, we measure cell rearrangements within a collective sheet. Finally, finite-time Lyapunov exponent (FTLE) values capture the stretching of the flow field and reflect its chaotic character. Applying collective migration analysis techniques to experimental data on both malignant and non-malignant human breast epithelial cells reveals differences in collective behavior that are not found from analyzing migration speeds alone. Non-malignant cells show increased cooperative motion on long time scales whereas malignant cells remain uncooperative as time progresses. Combining multiple analysis techniques also shows that these two cell types differ in their response to a perturbation of cell-cell adhesion through the molecule E-cadherin. Non-malignant MCF10A cells use E-cadherin for short time coordination of collective motion, yet even with decreased E-cadherin expression, the cells remain coordinated over long time scales. In contrast, the migration behavior of malignant and invasive MCF10CA1a cells, which already shows decreased collective dynamics on both time scales, is insensitive to the change in E-cadherin expression.

Probabilistic Imaging and Dynamic Modeling of Earthquake Source Processes

Investigation of large, destructive earthquakes is challenged by their infrequent occurrence and the remote nature of geophysical observations. This thesis sheds light on the source processes of large earthquakes from two perspectives: robust and quantitative observational constraints through Bayesian inference for earthquake source models, and physical insights on the interconnections of seismic and aseismic fault behavior from elastodynamic modeling of earthquake ruptures and aseismic processes.

To constrain the shallow deformation during megathrust events, we develop semi-analytical and numerical Bayesian approaches to explore the maximum resolution of the tsunami data, with a focus on incorporating the uncertainty in the forward modeling. These methodologies are then applied to invert for the coseismic seafloor displacement field in the 2011 Mw 9.0 Tohoku-Oki earthquake using near-field tsunami waveforms and for the coseismic fault slip models in the 2010 Mw 8.8 Maule earthquake with complementary tsunami and geodetic observations. From posterior estimates of model parameters and their uncertainties, we are able to quantitatively constrain the near-trench profiles of seafloor displacement and fault slip. Similar characteristic patterns emerge during both events, featuring the peak of uplift near the edge of the accretionary wedge with a decay toward the trench axis, with implications for fault failure and tsunamigenic mechanisms of megathrust earthquakes.

To understand the behavior of earthquakes at the base of the seismogenic zone on continental strike-slip faults, we simulate the interactions of dynamic earthquake rupture, aseismic slip, and heterogeneity in rate-and-state fault models coupled with shear heating. Our study explains the long-standing enigma of seismic quiescence on major fault segments known to have hosted large earthquakes by deeper penetration of large earthquakes below the seismogenic zone, where mature faults have well-localized creeping extensions. This conclusion is supported by the simulated relationship between seismicity and large earthquakes as well as by observations from recent large events. We also use the modeling to connect the geodetic observables of fault locking with the behavior of seismicity in numerical models, investigating how a combination of interseismic geodetic and seismological estimates could constrain the locked-creeping transition of faults and potentially their co- and post-seismic behavior.

Guided Migration and Collective Behavior: Cell Dynamics during Cancer Progression

This dissertation focuses on gaining understanding of cell migration and collective behavior through a combination of experiment, analysis, and modeling techniques. Cell migration is a ubiquitous process that plays an important role during embryonic development and wound healing as well as in diseases like cancer, which is a particular focus of this work. As cancer cells become increasingly malignant, they acquire the ability to migrate away from the primary tumor and spread throughout the body to form metastatic tumors. During this process, changes in gene expression and the surrounding tumor environment can lead to changes in cell migration characteristics. In this thesis, I analyze how cells are guided by the texture of their environment and how cells cooperate with their neighbors to move collectively. The emergent properties of collectively moving groups are a particular focus of this work as collective cell dynamics are known to change in diseases such as cancer. The internal machinery for cell migration involves polymerization of the actin cytoskeleton to create protrusions that---in coordination with retraction of the rear of the cell---lead to cell motion. This actin machinery has been previously shown to respond to the topography of the surrounding surface, leading to guided migration of amoeboid cells. Here we show that epithelial cells on nanoscale ridge structures also show changes in the morphology of their cytoskeletons; actin is found to align with the ridge structures. The migration of the cells is also guided preferentially along the ridge length. These ridge structures are on length scales similar to those found in tumor microenvironments and as such provide a system for studying the response of the cells' internal migration machinery to physiologically relevant topographical cues. In addition to sensing surface topography, individual cells can also be influenced by the pushing and pulling of neighboring cells. The emergent properties of collectively migrating cells show interesting dynamics and are relevant for cancer progression, but have been less studied than the motion of individual cells. We use Particle Image Velocimetry (PIV) to extract the motion of a collectively migrating cell sheet from time lapse images. The resulting flow fields allow us to analyze collective behavior over multiple length and time scales. To analyze the connection between individual cell properties and collective migration behavior, we compare experimental flow fields with the migration of simulated cell groups. Our collective migration metrics allow for a quantitative comparison between experimental and simulated results. This comparison shows that tissue-scale decreases in collective behavior can result from changes in individual cell activity without the need to postulate the existence of subpopulations of leader cells or global gradients. In addition to tissue-scale trends in collective behavior, the migration of cell groups includes localized dynamic features such as cell rearrangements. An individual cell may smoothly follow the motion of its neighbors (affine motion) or move in a more individualistic manner (non-affine motion). By decomposing individual motion into both affine and non-affine components, we measure cell rearrangements within a collective sheet. Finally, finite-time Lyapunov exponent (FTLE) values capture the stretching of the flow field and reflect its chaotic character. Applying collective migration analysis techniques to experimental data on both malignant and non-malignant human breast epithelial cells reveals differences in collective behavior that are not found from analyzing migration speeds alone. Non-malignant cells show increased cooperative motion on long time scales whereas malignant cells remain uncooperative as time progresses. Combining multiple analysis techniques also shows that these two cell types differ in their response to a perturbation of cell-cell adhesion through the molecule E-cadherin. Non-malignant MCF10A cells use E-cadherin for short time coordination of collective motion, yet even with decreased E-cadherin expression, the cells remain coordinated over long time scales. In contrast, the migration behavior of malignant and invasive MCF10CA1a cells, which already shows decreased collective dynamics on both time scales, is insensitive to the change in E-cadherin expression.

Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data.

Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)?height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems.

A demand-driven analysis of tourist accommodation price: A quantile regression of room bookings

Tourist accommodation expenditure is a widely investigated topic as it represents a major contribution to the total tourist expenditure. The identification of the determinant factors is commonly based on supply-driven applications while little research has been made on important travel characteristics. This paper proposes a demand-driven analysis of tourist accommodation price by focusing on data generated from room bookings. The investigation focuses on modeling the relationship between key travel characteristics and the price paid to book the accommodation. To accommodate the distributional characteristics of the expenditure variable, the analysis is based on the estimation of a quantile regression model. The findings support the econometric approach used and enable the elaboration of relevant managerial implications.

Spatio-temporal modeling of data imputation for daily rainfall series in homogeneous zones.

Spatio-temporal modelling is an area of increasing importance in which models and methods have often been developed to deal with specific applications. In this study, a spatio-temporal model was used to estimate daily rainfall data. Rainfall records from several weather stations, obtained from the Agritempo system for two climatic homogeneous zones, were used. Rainfall values obtained for two fixed dates (January 1 and May 1, 2012) using the spatio-temporal model were compared with the geostatisticals techniques of ordinary kriging and ordinary cokriging with altitude as auxiliary variable. The spatio-temporal model was more than 17% better at producing estimates of daily precipitation compared to kriging and cokriging in the first zone and more than 18% in the second zone. The spatio-temporal model proved to be a versatile technique, adapting to different seasons and dates.

MODELING AND EXPERIMENTAL SETUP OF AN HCCI ENGINE

For the past three decades the automotive industry is facing two main conflicting challenges to improve fuel economy and meet emissions standards. This has driven the engineers and researchers around the world to develop engines and powertrain which can meet these two daunting challenges. Focusing on the internal combustion engines there are very few options to enhance their performance beyond the current standards without increasing the price considerably. The Homogeneous Charge Compression Ignition (HCCI) engine technology is one of the combustion techniques which has the potential to partially meet the current critical challenges including CAFE standards and stringent EPA emissions standards. HCCI works on very lean mixtures compared to current SI engines, resulting in very low combustion temperatures and ultra-low NOx emissions. These engines when controlled accurately result in ultra-low soot formation. On the other hand HCCI engines face a problem of high unburnt hydrocarbon and carbon monoxide emissions. This technology also faces acute combustion controls problem, which if not dealt properly with yields highly unfavorable operating conditions and exhaust emissions. This thesis contains two main parts. One part deals in developing an HCCI experimental setup and the other focusses on developing a grey box modelling technique to control HCCI exhaust gas emissions. The experimental part gives the complete details on modification made on the stock engine to run in HCCI mode. This part also comprises details and specifications of all the sensors, actuators and other auxiliary parts attached to the conventional SI engine in order to run and monitor the engine in SI mode and future SI-HCCI mode switching studies. In the latter part around 600 data points from two different HCCI setups for two different engines are studied. A grey-box model for emission prediction is developed. The grey box model is trained with the use of 75% data and the remaining data is used for validation purpose. An average of 70% increase in accuracy for predicting engine performance is found while using the grey-box over an empirical (black box) model during this study. The grey-box model provides a solution for the difficulty faced for real time control of an HCCI engine. The grey-box model in this thesis is the first study in literature to develop a control oriented model for predicting HCCI engine emissions for control.

MOLECULAR MODELING OF EPON 862-DETDA / CARBON COMPOSITES

The thermoset epoxy resin EPON 862, coupled with the DETDA hardening agent, are utilized as the polymer matrix component in many graphite (carbon fiber) composites. Because it is difficult to experimentally characterize the interfacial region, computational molecular modeling is a necessary tool for understanding the influence of the interfacial molecular structure on bulk-level material properties. The purpose of this research is to investigate the many possible variables that may influence the interfacial structure and the effect they will have on the mechanical behavior of the bulk level composite. Molecular models are established for EPON 862-DETDA polymer in the presence of a graphite surface. Material characteristics such as polymer mass-density, residual stresses, and molecular potential energy are investigated near the polymer/fiber interface. Because the exact degree of crosslinking in these thermoset systems is not known, many different crosslink densities (degrees of curing) are investigated. It is determined that a region exists near the carbon fiber surface in which the polymer mass density is different than that of the bulk mass density. These surface effects extend ~10 Å into the polymer from the center of the outermost graphite layer. Early simulations predict polymer residual stress levels to be higher near the graphite surface. It is also seen that the molecular potential energy in polymer atoms decreases with increasing crosslink density. New models are then established in order to investigate the interface between EPON 862-DETDA polymer and graphene nanoplatelets (GNPs) of various atomic thicknesses. Mechanical properties are extracted from the models using Molecular Dynamics techniques. These properties are then implemented into micromechanics software that utilizes the generalized method of cells to create representations of macro-scale composites. Micromechanics models are created representing GNP doped epoxy with varying number of graphene layers and interfacial polymer crosslink densities. The initial micromechanics results for the GNP doped epoxy are then taken to represent the matrix component and are re-run through the micromechanics software with the addition of a carbon fiber to simulate a GNP doped epoxy/carbon fiber composite. Micromechanics results agree well with experimental data, and indicate GNPs of 1 to 2 atomic layers to be highly favorable. The effect of oxygen bonded to the surface of the GNPs is lastly investigated. Molecular Models are created for systems with varying graphene atomic thickness, along with different amounts of oxygen species attached to them. Models are created for graphene containing hydroxyl groups only, epoxide groups only, and a combination of epoxide and hydroxyl groups. Results show models of oxidized graphene to decrease in both tensile and shear modulus. Attaching only epoxide groups gives the best results for mechanical properties, though pristine graphene is still favored.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.