Inter-cloud is a recently emerging approach that expands cloud elasticity. By facilitating an adaptable setting, it purposes at the realization of a scalable resource provisioning that enables a diversity of cloud user requirements to be handled efficiently. This study’s contribution is in the inter-cloud performance optimization of job executions using metascheduling concepts. This includes the development of the inter-cloud meta-scheduling (ICMS) framework, the ICMS optimal schemes and the SimIC toolkit. The ICMS model is an architectural strategy for managing and scheduling user services in virtualized dynamically inter-linked clouds. This is achieved by the development of a model that includes a set of algorithms, namely the Service-Request, Service-Distribution, Service-Availability and Service-Allocation algorithms. These along with resource management optimal schemes offer the novel functionalities of the ICMS where the message exchanging implements the job distributions method, the VM deployment offers the VM management features and the local resource management system details the management of the local cloud schedulers. The generated system offers great flexibility by facilitating a lightweight resource management methodology while at the same time handling the heterogeneity of different clouds through advanced service level agreement coordination. Experimental results are productive as the proposed ICMS model achieves enhancement of the performance of service distribution for a variety of criteria such as service execution times, makespan, turnaround times, utilization levels and energy consumption rates for various inter-cloud entities, e.g. users, hosts and VMs. For example, ICMS optimizes the performance of a non-meta-brokering inter-cloud by 3%, while ICMS with full optimal schemes achieves 9% optimization for the same configurations. The whole experimental platform is implemented into the inter-cloud Simulation toolkit (SimIC) developed by the author, which is a discrete event simulation framework.

The uncertainty of predicted fluxes, variables of state, cloud- and precipitation formation to the choice of plant- and soil parameters, soil type, subgrid-scale heterogeneity and inhomogeneity is examined by results of simulations wherein one parameter is alternatively changed for all grid cells of the domain within its natural range of variability. A higher sensitivity of evapotranspiration to evaporative conductivity, roughness length, and subgridscale heterogeneity or inhomogeneity is found than for the other parameters examined. Except for wind velocity, the quantities of liquid and solid water substances the uncertainty is lower at night and early afternoon than at other times. Therefore, evaluation of meteorological models should be performed at those times of the day when the uncertainty in model results caused by the choice of plant and soil parameters is at a minimum. Furthermore, domainspecific parameters should be used when ever available. / Die Unsicherheit in simulierten Flüssen, Zustandsgrößen, Wolken- und Niederschlagsbildung durch die Wahl der Pflanzen- und Bodenparameter, Bodentypen, subskaligen Heterogenität und Inhomogenität wird mit Hilfe von Ergebnissen aus Simulationen untersucht, in denen alternativ für alle Gitterzellen ein Parameter im Rahmen seiner natürlichen Variabilität verändert wird. So besteht eine höhere Sensitivität der Evapotranspiration zur Verdunstungsleitfähigkeit, Rauhigkeitslänge und subskaligen Heterogenität oder Inhomogenität als zu den anderen untersuchten Größen. Windgeschwindigkeit, die Menge an flüssigen und festen Wassersubstanzen ausgenommen, ist die Unsicherheit nachts und am frühen Nachmittag geringer als zur übrigen Zeit. Deshalb sollten Modellevaluationen möglichst zum Zeitpunkt geringer Unsicherheit durchgeführt werden. Darüber hinaus sollten, wenn möglich, gebietsspezifische Parameter verwendet werden.

Cloud computing at a high level comprises of the availability of hardware, software and technical support via a network protocol to a remote client on a pay-per-use basis. Businesses using Cloud resources has been increasing steadily in the very recent past and the number of Cloud service providers (CSP) are increasing as well. The challenges that characterize a Cloud data center include: on-demand service, elasticity, resources pooling, broad network access, service meters. As the customer base is in creasing and their resource requirement and usage pattern has been becoming highly volatile, proper utilization of the resources and generating revenue by appropriately charging the clients for their uses has become an even more challenging research problem. In other words, Cloud resource pricing has emerged as an important and pressing problem to study for ever increasing utility of Cloud computing. Literature review reveals that there are economy-based models (cash flow, net present value etc.) used for charging mechanism suggested by many researchers. Most of these models are rigid that they are not build with the core of Cloud - elasticity in mind. Also, the economic models do not provide flexibility of the economy of scale to either increase or decrease the resource requirement and appropriately charge for such increase or decrease in resource use. For my thesis, I have designed and developed a Cloud resources pricing model that satisfies two important constraints: the dynamic ability of the model to provide a high satisfaction guarantee measured as Quality of Service (QoS) - from users perspectives, and profitability constraints - from the Cloud service providers perspectives. I have employed financial option theory and treated the Cloud resources as underlying assets to capture the realistic value of the Cloud Compute Commodities (C3). I have priced the Cloud resources using my model. Through this research, I show that the Cloud parameters can be mapped to financial economic model and that this model can be effectively implemented for resource pricing purpose. I discuss the results of pricing Cloud Compute Commodities (C3) for various input parameters, such as the age of the resource and quality of service. / February 2016

Cloud services are popular for hosting applications because they offer simplicity and cost efficiency. There are a lot of different providers offering many different services, which can make it hard to find the one most suitable for you. To make informed decisions you need to evaluate different services. This project evaluates a cloud service, called Amazon Lambda, from one of the biggest cloud service providers. Amazon Lambda is a simple service which runs one function in response to an event. In this project it is evaluated on suitability, performance and cost. To evaluate suitability, a certain kind of applications, games, were selected. The game industry is innovative and put high requirements on performance. A few simple Lambda functions were implemented and integrated in a prototype game. Some calculations were made for determining the cost of hosting such a game on Amazon Lambda. A few tests were implemented and run in order to further evaluate the performance.

The design and implementation of today's most advanced VLSI circuits and multi-layer printed circuit boards would not be possible without automated design tools that assist with the placement of components and the routing of connections between these components. In this work, we investigate how placement and routing can be implemented and accelerated using cloud computing resources. A parallel genetic algorithm approach is used to optimize component placement and the routing order supplied to a Lee's algorithm maze router. A study of mutation rate, dominance rate, and population size is presented to suggest favorable parameter values for arbitrary-sized printed circuit board problems. The algorithm is then used to successfully design a Microchip PIC18 breakout board and Micrel Ethernet Switch. Performance results demonstrate that a 50X runtime performance improvement over a serial approach is achievable using 64 cloud computing cores. The results further suggest that significantly greater performance could be achieved by requesting additional cloud computing resources for additional cost. It is our hope that this work will serve as a framework for future efforts to improve parallel placement and routing algorithms using cloud computing resources.

The effects of aerosol in the atmosphere account for some of the largest uncertainties in estimates of the human impact on climate. These effects depend not only on the total mass of aerosol, but also its size distribution, mixing state and vertical profile. Previous studies have suggested that both the size distribution and mixing state of aerosol may be strongly influenced by repeated cycling through non-precipitating cloud. The extent of this process is assessed in the HadGEM3–UKCA model; although fewer cycles are seen for all aerosol than in previous studies, the figure varies considerably between aerosol types. The role of scavenging by precipitating cloud is also considered, and several approaches to increasing the physical realism of its representation are considered. In particular, coupling convective scavenging into the convective transport scheme is shown to provide significant benefits over an operator-split approach (which underestimates removal and allows excess aerosol to reach the upper troposphere and be transported to remote regions). To evaluate the alternative convective scavenging schemes, a method is developed for carrying out a pointwise evaluation against vertically-resolved in-situ observations from large-scale aircraft campaigns, based on nudging and flight-track sampling in the model. It is demonstrated that this approach can help to constrain the choice between different model configurations with a degree of statistical confidence. Finally, the processes controlling the vertical profile of aerosol are investigated using a series of model-based sensitivity tests, along with the extent to which these processes can account for the large diversity in vertical profiles seen amongst current models. For mass profiles and number profiles of large particles (greater than about 100nm dry diameter), removal and secondary production processes are shown to be most important; for number profiles of smaller particles, microphysical processes are shown to become increasingly dominant.

We examine three case studies during the Deep Convective Clouds and Chemistry (DC3) field experiment when storm inflow and outflow air were sampled for aerosol subsaturated hygroscopicity and the real part of refractive index (n) with a Differential Aerosol Sizing and Hygroscopicity Probe (DASH-SP) on the NASA DC-8. Relative to inflow aerosol particles, outflow particles were more hygroscopic (by 0.03 based on the estimated parameter) in one of the three storms examined. Two of three control flights with no storm convection reveal higher values, albeit by only 0.02, at high altitude (> 8km) versus < 4km. Entrainment modeling shows that measured values in the outflow of the three storm flights are higher than predicted values (by 0.03-0.11) based on knowledge of values from the inflow and clear air adjacent to the storms. This suggests that other process(es) contributed to hygroscopicity enhancements such as secondary aerosol formation via aqueous-phase chemistry. Values of n were higher in the outflow of two of the three storm flights, reaching as high as 1.54. More statistically significant differences were observed in control flights (no storms) where n decreased from 1.50-1.52 (< 4km) to 1.49-1.50 (> 8km). Chemical data show that enhanced hygroscopicity was coincident with lower organic mass fractions, higher sulfate mass fractions, and higher O:C ratios of organic aerosol. Refractive index did not correlate as well with available chemical data. Deep convection is shown to alter aerosol radiative properties, which has implications for aerosol effects on climate.

We report the discovery of the minor planet 2013 SY99 on an exceptionally distant, highly eccentric orbit. With a perihelion of 50.0. au, 2013 SY99' s orbit has a semimajor axis of 730 +/- 40. au, the largest known for a high-perihelion trans-Neptunian object (TNO), and well beyond those of (90377) Sedna and 2012 VP113. Yet, with an aphelion of 1420 +/- 90. au, 2013 SY99' s orbit is interior to the region influenced by Galactic tides. Such TNOs are not thought to be produced in the current known planetary architecture of the solar system, and they have informed the recent debate on the existence of a distant giant planet. Photometry from the Canada-France-Hawaii Telescope, Gemini North, and Subaru indicate 2013 SY99 is similar to 250. km in diameter and moderately red in color, similar to other dynamically excited TNOs. Our dynamical simulations show that Neptune's weak influence during 2013 SY99' s perihelia encounters drives diffusion in its semimajor axis of hundreds of astronomical units over 4. Gyr. The overall symmetry of random walks in the semimajor axis allows diffusion to populate 2013 SY99' s orbital parameter space from the 1000 to 2000. au inner fringe of the Oort cloud. Diffusion affects other known TNOs on orbits with perihelia of 45 to 49. au and semimajor axes beyond 250. au. This provides a formation mechanism that implies an extended population, gently cycling into and returning from the inner fringe of the Oort cloud.

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Primary goal of my thesis is managerial recommendation, which should ease the management decision making process whether AutoCont would add Private cloud (SaaS) option of project management software in its service portfolio. There will be presented the Total Cost of Ownership analysis for both On premise and Private cloud option on an example of SME in practical part.