Universiẗat, Diss., 2003--Essen.

Techn. Universiẗat, Diss., 2004--Darmstadt.

This work contains the design solution and implementation of importing data mining tool Weka to the Visual Studio and Microsoft SQL Server 2012 by Managed Plug-in algorithm. In this thesis is describe, how is possible to create new Managed Plug-In algorithm and how is possible to import tool Weka into it. To use tool Weka in the new Managed Plugin Algorithm, is used the IKVM port. The IKVM can create from Weka tool new C# library, which can be used by Managed Plug-in Algorithm.

This thesis deals with different approaches in the development of an analytical model of the data warehouse with a focus on tabular mode and its associated technologies and tools from the Microsoft company. In the theoretical part the thesis introduces the principles of Business Intelligence and also the concept of the semantic model. It also states tabular model as a new approach of creating an analytical database stored in the RAM memory. The tabular model is developed on the top of the data warehouse of a fictitious company Contoso in the practical part. The emphasis is put mainly on the comparison between the tabular and multidimensional model. The work ends with deploying both models on a virtual server with a comparison of their computing power for each of the designed scenarios.

The diploma thesis deals with design and implementation of cooperative work on documents in the application TEXonWeb. The diploma theses includes the analysis of competitive solutions and key technologies for implementation. The cooperative work on documents is a functionality which allows users to cooperate simply on one document in real-time. Text of the thesis describes the process of implementation using the technology which is described in the initial analysis. The diploma theses also includes some ideas for next possible development.

The theoretical part of this thesis is devoted to familiarization with the technology of cloud computing, virtualization technology, data storage problems and Petri nets. Followed by the practical part, the first part discusses the various technologies which are VMware ESX, Citrix Xen, Hyper -V, KVM and oVirt. The following part is a summary of the characteristics of each technology and an output of this summary is a decision tree which is intended to help to select the appropriate virtualization technology. The second part is dedicated to the implementation of an application used for storing data in the database depending on the sensitivity of data. The thesis includes the final economic evaluation and discussion as well.

The diploma thesis deals with the possibilities of using application virtualization in the Windows environment. The theoretical part describes the operating principle of application virtualization. Furthermore, there are specified the individual characteristics and the reasons for the move to application virtualization. The author provides the basic overview and comparison of the specific ways of virtualization. The appropriate solution of the practical part was based on the Saaty method. In the practical part, the author describes in detail how to implement and use the chosen technology. Based on practical experience, he assesses functioning of the solution. The final part of the thesis includes both general and specific recommendations for the use of application virtualization.

There are a number of known architectural patterns for TCP/IP server design. I present a survey of design choices based on some of the most common of these patterns. I have demonstrated, with working code samples, that most of these architectural patterns are readily portable between UNIX and Windows NT platforms without necessarily incurring significant performance penalties. / Computing / M. Sc. (Computer Science)

The theoretical part of this thesis is focused on possibilities of the WebML modeling language and the WebRatio CASE tool. The practical part of this thesis deals with design and analysis of web application supporting the processes associated with the telecommunications and processing of data obtained by PBX. For the process will be used WebML based on acquired knowledge.

The emerging general purpose graphics processing units (GPGPU) computing has tremendously speeded up a great variety of commercial and scientific applications. The GPUs have become prevalent accelerators in current high performance clusters. Though the computational capacity per Watt of the GPUs is much higher than that of the CPUs, the hybrid GPU clusters still consume enormous power. To conserve energy on this kind of clusters is of critical significance. In this thesis, we seek energy conservative computing on the GPU accelerated servers. We introduce our studies as follows. First, we dissect the GPU memory hierarchy due to the fact that most of the GPU applications are suffering from the GPU memory bottleneck. We find that the conventional CPU cache models cannot be applied on the modern GPU caches, and the microbenchmarks to study the conventional CPU cache become invalid for the GPU. We propose the GPU-specified microbenchmarks to examine the GPU memory structures and properties. Our benchmark results verify that the design goal of the GPU has transformed from pure computation performance to better energy efficiency. Second, we investigate the impact of dynamic voltage and frequency scaling (DVFS), a successful energy management technique for CPUs, on the GPU platforms. Our experimental results suggest that GPU DVFS is still promising in conserving energy, but the patterns to save energy strongly differ from those of the CPU. Besides, the effect of GPU DVFS depends on the individual application characteristics. Third, we derive the GPU DVFS power and performance models from our experimental results, based on which we find the optimal GPU voltage and frequency setting to minimize the energy consumption of a single GPU task. We then study the problem of scheduling multiple tasks on a hybrid CPU-GPU cluster to minimize the total energy consumption by GPU DVFS. We design an effective offline scheduling algorithm which can reduce the energy consumption significantly. At last, we combine the GPU DVFS and dynamic resource sleep (DRS), another energy management technique, to further conserve the energy, for the online task scheduling on hybrid clusters. Though the idle energy consumption increases significantly compared to the offline problem, our online scheduling algorithm still achieves more than 30% of energy conservation with appropriate runtime GPU DVFS readjustments.