In order to improve simulation performance and integrate simulation resources among geographically distributed locations, the concept of distributed simulation is proposed. Several types of distributed simulation standards, such as DIS and HLA are established to formalize simulations and achieve reusability and interoperability of simulation components. In order to implement these distributed simulation standards and manage the underlying system of distributed simulation applications, Grid Computing and Cloud Computing technologies are employed to tackle the details of operation, configuration, and maintenance of the simulation platforms in which simulation applications are deployed. However, for modelers who may not be familiar with the management of distributed systems, it is challenging to create a simulation-run-ready environment that incorporates different types of computing resources and network environments. In this thesis, we propose a new multi-layered cloud-based scheme for enabling modeling and simulation based on different distributed simulation standards. The scheme is designed to ease the management of underlying resources and achieve rapid elasticity, providing unlimited computing capability to end users; energy consumption, security, multi-user availability, scalability and deployment issues are all considered. We describe a mechanism for handling diverse network environments. With its adoption, idle public resources can easily be configured as additional computing capabilities for the local resource pool. A fast deployment model is built to relieve the migration and installation process of this platform. An energy conservation strategy is utilized to reduce the energy consumption of computing resources. Security components are also implemented to protect sensitive information and block malicious attacks in the cloud. In the experiments, the proposed scheme is compared with its corresponding grid computing platform; the cloud computing platform achieves a similar performance, but incorporates many of the cloud's advantages.

The past few years have witnessed a rapid emergence of large-scale, geographically dispersed, clouds offering in the form of an Infrastructure-as-a-Service (IaaS). The adoption of these services requires the deployment of new networking technologies. This in turn, ensures the performance of the offered cloud services. Network virtualization has been proposed as a key attribute of the future inter-networking paradigm, providing efficient resource management solutions. Among the challenges that need yet to be addressed is the necessity to provide dynamic quality differentiated network services. In addition, it is required to guarantee the availability of network resources in response to workload fluctuations. Finally, it is necessary to periodically re-optimize the resource provisioning to be able to provide efficient resource utilization. These challenges are the motivation behind this work which aimed at developing a novel adaptive resource management model based on network virtualization. First, the proposed work describes a novel Virtual-Network-as-a-Service (VNaaS) model offering differentiated network-aware cloud services, resulting in a guarantee for the quality of the offered applications. This is achieved by enabling the cloud application providers to accurately express their dynamic needs, demand constraints and their network latency tolerance. The proposed work also enables the infrastructure provider to offer Elasticity-as-a-Service (EaaS) for the communication links by estimating and reserving the adequate pool of resources needed to fulfill the network workload fluctuations. This EaaS is offered at differentiated levels according to the hosted applications bandwidth-sensitivity. Finally, the proposed work employs a novel network resource re-optimization technique. The latter efficiently performs rearrangement for the VN portions contributing to the fragmentation of the underlying network. Simulation results demonstrate the effectiveness of the presented work and the significant gains achieved in terms of better adaptive network resource management.

Cloud computing environments mainly focus on the delivery of resources, platforms, and infrastructure as services to users over the Internet. More specifically, Cloud promises user access to a scalable amount of resources, making use of the elasticity on the provisioning of recourses by scaling them up and down depending on the demand. The cloud technology has gained popularity in recent years as the next big step in the IT industry. The number of users of Cloud services has been increasing steadily, so the need for efficient task scheduling is crucial for improving and maintaining performance. Moreover, those users have different SLAs that imposes different demands on the cloud system. In this particular case, a scheduler is responsible for assigning tasks to virtual machines in an effective and efficient matter to meet with the QoS promised to users. The scheduler needs to adapt to changes in the cloud environment along with defined demand requirements. Hence, an Adjustable and Configurable bio-inspired scheduling heuristic for cloud based systems (ACBH) is suggested. We also present an extensively comparative performance study on bio-inspired scheduling algorithms namely Ant Colony Optimization (ACO) and Honey Bee Optimization (HBO). Furthermore, a networking scheduling algorithm is also evaluated, which comprises Random Biased Sampling (RBS). The study of bio-inspired techniques concluded that all the bio-inspired algorithms follow the same flow that was later used in the development of (ACBH). The experimental results have shown that ACBH has a 90% better execution time that it closest rival which is ACO. ACBH has a better performance in terms of the fairness between execution time differences between tasks. HBO shows better scheduling when the objective consists mainly of costs. However, when there is multiple optimization objectives ACBH performs the best due to its configurability and adaptability.

Distributed simulation over Cloud environment is still a new subject. Cloud computing is expected to bring new benefits to conventional distributed simulation, including elasticity on computation resource, cost saving on investment and convenience on service accessibility. A few researches have been done on applying Cloud computing on distributed simulation. However, there are various drawbacks and limitations on those works. Lack of interoperability across Cloud platforms is one of critical drawbacks among them. It can greatly limit the usability and flexibility of distributed simulation over Cloud environment. Based on the investigation on Cloud computing and existing distributed simulation systems over Cloud environment, a novel interoperable HLA-based (High Level Architecture) simulation system over a Cloud environment, ISSC (Interoperable Simulation System over a Cloud Environment), is proposed in this thesis. ISSC aims to address the interoperability issue of simulation system across various Cloud platforms. It employs OCCI and a set of technologies, including Ruby on Rails, OpenVPN and RESTful web services, to build the interoperability across Cloud platforms. It adopts a distributed architecture to construct flexibility and expansibility of the system. The prototype and related experiments performed provides an excellent demonstration that ISSC is a reliable and effective solution on interoperable simulation system over a diverse Cloud environment.

The thesis deals with decision-making about how to operate the system for communication and collaboration in a company - groupware. Compares two different ways of operating systems. Using cloud-based services and systems which the company operates on its own hardware. In the thesis the decision-making model is created. The essential criterion for decision making are total cost of ownership (TCO), the work therefore contains a procedure to calculate the TCO. Decision-making model and the calculation procedure is tested using simulation and in the case study. The simulation also responds to issues of economic suitability cloud-based systems in small and in large companies.

The thesis deals with the means of using cloud computing in business. The aim is to present the selected cloud services currently available on the market and then compare and evaluate them according to certain criteria. After the characteristics of the technology have been listed, including its advantages and disadvantages, a detailed description of the chosen products follows. Specifically, these are Microsoft Office 365, G Suite (formerly also known as Google Apps) and Dropbox Business. In the next section of the thesis, the services are evaluated in each of the selected criteria using the Kepner-Tregoe decision analysis. The list of the criteria includes, among others, the usability of the user interface, security, or deployment in a company.

In the thesis, the author wanted to research the topic of implementation of cloud-based solutions in the area of Supply Chain Management. The first part is devoted to theoretical background of Cloud Computing -- different types of solutions are suitable for various kinds of business activity, and Supply Chain Management. In the second part, the author focused on an analysis of a company - FN Granum and searched for a Cloud Computing solution, which would be the best for it, taking into account current situation and future plans of the entity. Such extensive research resulted in a set of conclusions and recommendations, which may be helpful for any manager considering an implementation of cloud-based solutions into their supply chains.

The thesis focuses on software piracy mainly in the context of cloud computing. Its goals are to analyze contemporary software piracy with the focus on its forms, reasons leading to it and on antipiracy techniques. Second part of the thesis defines cloud computing and focuses on two basic interdependencies with software piracy: cloud as a pirates' tool and cloud as means to fight the piracy. Based on the findings of previous parts, concluding part of the thesis suggests set of recommendations for involved parties (especially software providers, cloud service providers and cloud service users).

The combination of Cloud Computing and Business Intelligence (BI) gives many advantages, however the classic deployment model is still in many cases preferred. Both models differ in many aspects and are suitable for various organizations. The thesis looks at these aspects and analyzes the differences that cloud computing brings to the Business Intelligence. Aims of the thesis are to compare both models according the architectural, security, user and financial aspects, influencing of the cloud computing on the implementation of BI and presenting some practical examples of BI solution. To achieve the aims the author uses a literature review, electronic sources and own analysis. Based on the identified impact of the cloud factor the author extends the reference model Management of Business Informatics (MBI).

Thesis describes the concept of cloud computing and its development into today's form. First section defines cloud computing through several definitions. It also describes important capabilities of the technology. Second section is presenting case studies of cloud deployment in Czech companies. It is evaluating advantages and disadvantages of the particular solution in use. Third section is presenting results of quantitative research that was carried out on this topic with IT professionals working in Czech private sector.