Cloud computing has a significant impact on information and communication technology (ICT) and is one of the most important technological drivers of the digitalization of enterprises. However, due to the increasing dissemination of cloud services and the growing number of cloud service providers (CSPs), the uncertainty and risks for user companies in adopting cloud services have also increased. In this paper, we address those aspects from the perspective of the CSPs. We identified relevant literature and studies and conducted interviews with business experts from 16 German CSPs. In our results, we present current customer requirements and barriers to using cloud services from a provider’s viewpoint and identify the actions of and obstacles for CSPs in meeting the needs and constraints of the customers. Finally, we identify current and future challenges for CSPs in dealing with customer requirements and barriers by addressing their root causes. One of the main challenges from the CSPs’ perspective is addressing customers appropriately and building relationships of trust. This also “forces” changes in the sales processes. In this process, the essential challenges can be identified as an increase in complexity and a simultaneous simplification of specific sales activities. Therefore, the necessity arises for the continuous support of business relationships through value-adding and additional services. However, this results in another challenge for the CSPs – Namely, to find the right balance between standardization and meeting customer-specific requirements. In our paper, we show that the perspective of the CSPs is rarely discussed in the literature. Nevertheless, understanding the perceptions of the providers and their actions and measures is essential for future research activities in the field of cloud service selection. Comparing the customers’ perspectives and viewpoints with the CSPs’ actions will enhance the development of a holistic selection approach for future cloud projects. Therefore, our paper’s contribution to research is also the identification of this missing integration.

This thesis describes existing cloud storage systems. Description of the prerequisites of developing cloud and distributed systems are presented. Current storage systems such as Dropbox, iCloud and Google drive are described. Description is mainly focused on the resources of both protocols and derive a conclusion for the use in the cloud storage systems. The practical part of this work is focused on creating two labs, what will be implemented in the teaching syllabus of Projecting, Administration and Security of Computer Networks subject. The first of the labs is focused on the implementation of own cloud services. In the last lab students attention will concentrate on interception of communication secured with SSL protocol.

El propósito del presente documento es el desarrollo del proyecto académico que tiene como objetivo plantear un modelo de análisis predictivo soportado por una plataforma tecnológica Cloud basada en técnicas de Machine Learning para pronosticar el fondo acumulado de un afiliado que aporte a un sistema privado de pensiones. Esto surge ante el desconocimiento sobre cómo se administran las finanzas personales en los fondos privados de pensiones representan un riesgo en el manejo del futuro de cada individuo. Esta ausencia de control en la administración del fondo de cada afiliado conlleva a no saber en todo momento a lo qué están expuestos. En la actualidad existen herramientas propias de las entidades administradoras de fondos de pensiones que permiten simular y calcular tanto el fondo como la pensión que recibirá un jubilado, sin embargo, lo que no ofrecen estas herramientas es la posibilidad de proyectar el fondo a partir de factores de crecimiento bajo criterios propios del afiliado que le otorguen la posibilidad de comprar cuál fondo voluntario de pensiones puede ser el más beneficioso al final de la vida laboral. La tecnología ha evolucionado y se aprecia grandes avances con respecto a la captura y almacenamiento de información. Una de estas tecnologías utiliza Cloud Computing, la cual ha crecido en grandes proporciones en diversas industrias y atraen la atención de comunidades de investigación debido a su potencial para implementar soluciones tecnológicas a la medida El desarrollo de este proyecto se llevará a cabo durante los ciclos académicos 2018-1 y 2018-2, realizando el análisis de tecnologías Cloud que soportan el modelo de análisis predictivo soportado por una plataforma tecnológica cloud basada en machine learning para determinar el fondo acumulado de un afiliado que aporte a un sistema privado de pensiones y el diseño del modelo tecnológico propuesto durante el ciclo 2018-1. La validación del modelo tecnológico mediante el desarrollo de la aplicación móvil, la documentación de los resultados y el plan de continuidad del proyecto en el ciclo 2018-2. / The purpose of this document is the development of the academic project that aims to propose a model of predictive analysis supported by a platform based on the cloud in the automatic learning to determine the accumulated fund in a private pension system. This arises from the lack of knowledge about how to manage personal finances in private pension funds in the management of each individual's future. This lack of control in the administration of the fund of each affiliate entails a lack of knowledge at all times. Currently, there are own tools of pension fund management entities that allow simulating and calculating both the fund and the pension that a retiree will receive, however, what these tools do not offer is the possibility of projecting the fund from Growth factors under the affiliate's own criteria that give him the possibility of buying which voluntary pension fund can be the most beneficial at the end of working life. The technology has evolved, and great advances can be seen with respect to the capture and storage of information. One of these technologies uses Cloud Computing, which has grown in large proportions in different industries and attracts the attention of research communities due to its potential to implement tailored technological solutions. The development of this project will be carried out during the 2018-1 and 2018-2 academic cycles, performing the analysis of Cloud technologies that support the predictive analysis model supported by a cloud-based technology platform in machine learning to determine the accumulated fund of an affiliate that contributes to a private pension system and the design of the technological model proposed during the 2018-1 semester. The validation of the technological model through the development of the mobile application, the documentation of the results and the plan of continuity of the project in the 2018-2 semester. / Tesis

CloudMe is a cloud computing service used for business and home users. CloudMe facilitates the user to store their personal files like music, video, documents and images. The primary focus of this thesis is on music. The personal music files can be uploaded to CloudMe manually or by using CloudMe sync in any order just like in personal computer. CloudMe offers different services to access the cloud from other devices like smart phones, web browser and the home computer.   Sonos wireless HiFi system is a set of Sonos component interconnected with the mesh network with the primary function to play digital audio. The components include subwoofer, speakers and Bridges in order to connect to wireless speakers. Sonos system is connected to internet through Ethernet or via Wi-Fi. Sonos gives access to music libraries stored in computer, free Internet radio stations and additional music services. The controller for the complete system has various choices as iPhone, Android and other specific Sonos controllers.   However, with Sonos, a computer is considered necessary to be running all the time in order to access the personal music files from the personal computer. Combining CloudMe to Sonos allow the requirement of an always-on computer to be removed. Instead the selected personal music files can be stored with the user‟s private CloudMe account, and the music can be accessed from the cloud storage through the Internet at anytime.   The main objective of this thesis is to build the given APIs from the Sonos that are required in order to access CloudMe from Sonos. Each API handles specific task to present CloudMe through Sonos to the user. For example an API handles user authentication and another API handles the metadata accessing. All the APIs are implemented in the given server from CloudMe. This integration not only provides access roughly the way the music files are stored in the cloud, but also implemented in a way to accesses via categories like artist, albums, genre, composers and also the playlist stored in the cloud. In order to get this menu view of all the music, the metadata of the entire music library from CloudMe is accessed and programmed to differentiate music options in the menu.

The emerging concept of vehicle cloudification is a promising solution to deal with ever-growing computational and communication demands of connected vehicles. A key idea is to have connected vehicles in the vicinity form a cluster which is called vehicular micro cloud. Vehicles in this micro cloud collaborate with other cluster members over vehicle-to-vehicle (V2V) networks for collective data processing, shared data storage, collaborative sensing and communication services. A typical use case of vehicular micro cloud is creation of a regional distributed data storage service, where member vehicles of the cloud collaboratively keep data contents in their on-board data storage devices. This allows vehicles in and around the vehicular micro cloud to request the contents from the micro cloud member(s) over vehicle-to-vehicle networks, or even update the data on the spot. In this thesis, we will discuss the need for vehicular micro clouds, followed by the architecture, formation of the micro clouds, and feasibility of micro clouds. Furthermore, we will cover aspects of efficient data transmission between vehicles, how to increase the scalability and to make it time efficient and cost efficient on practical road conditions for moving vehicles by encouraging coordination between neighboring micro cloud to help transfer data .

Les thèmes de recherche abordés dans ce manuscrit ont trait à la parallélisation d’algorithmes de classification non-supervisée (clustering) sur des plateformes de Cloud Computing. Le chapitre 2 propose un tour d’horizon de ces technologies. Nous y présentons d’une manière générale le Cloud Computing comme plateforme de calcul. Le chapitre 3 présente l’offre cloud de Microsoft : Windows Azure. Le chapitre suivant analyse certains enjeux techniques de la conception d’applications cloud et propose certains éléments d’architecture logicielle pour de telles applications. Le chapitre 5 propose une analyse du premier algorithme de classification étudié : le Batch K-Means. En particulier, nous approfondissons comment les versions réparties de cet algorithme doivent être adaptées à une architecture cloud. Nous y montrons l’impact des coûts de communication sur l’efficacité de cet algorithme lorsque celui-ci est implémenté sur une plateforme cloud. Les chapitres 6 et 7 présentent un travail de parallélisation d’un autre algorithme de classification : l’algorithme de Vector Quantization (VQ). Dans le chapitre 6 nous explorons quels schémas de parallélisation sont susceptibles de fournir des résultats satisfaisants en terme d’accélération de la convergence. Le chapitre 7 présente une implémentation de ces schémas de parallélisation. Les détails pratiques de l’implémentation soulignent un résultat de première importance : c’est le caractère en ligne du VQ qui permet de proposer une implémentation asynchrone de l’algorithme réparti, supprimant ainsi une partie des problèmes de communication rencontrés lors de la parallélisation du Batch K-Means. / He subjects addressed in this thesis are inspired from research problems faced by the Lokad company. These problems are related to the challenge of designing efficient parallelization techniques of clustering algorithms on a Cloud Computing platform. Chapter 2 provides an introduction to the Cloud Computing technologies, especially the ones devoted to intensivecomputations. Chapter 3 details more specifically Microsoft Cloud Computing offer : Windows Azure. The following chapter details technical aspects of cloud application development and provides some cloud design patterns. Chapter 5 is dedicated to the parallelization of a well-known clustering algorithm: the Batch K-Means. It provides insights on the challenges of a cloud implementation of distributed Batch K-Means, especially the impact of communication costs on the implementation efficiency. Chapters 6 and 7 are devoted to the parallelization of another clustering algorithm, the Vector Quantization (VQ). Chapter 6 provides an analysis of different parallelization schemes of VQ and presents the various speedups to convergence provided by them. Chapter 7 provides a cloud implementation of these schemes. It highlights that it is the online nature of the VQ technique that enables an asynchronous cloud implementation, which drastically reducesthe communication costs introduced in Chapter 5.

M. Tech. (Department of Information Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Virtualization is the main technology that powers today’s cloud computing systems. Virtualization provides isolation as well as resource control that enable multiple workloads to run efficiently on a single shared machine and thus allows servers that traditionally require multiple physical machines to be consolidated to a single, cost-effective physical machine using virtual machines or containers. Due to virtual machine techniques, the strategies that improve performance like hardware acceleration, running concurrent virtual machines without the correct proper resource controls not used and correctly configured, the problems of scalability as well as service provisioning (crashing response time, resource contention and functionality or usability) for cloud computing, emanate from the configurations of the virtualized system. Virtualization performance is a critical factor in datacentre and cloud computing service delivery. To evaluate virtualization performance as well as to determine which virtual machine configuration provides effective performance, how to allocate and distribute resources for virtual machine performance equally is critical in this research study. In this study, datacentre purposed servers together with Type 1 (bare metal hypervisors), VMware ESXi 5.5, and Proxmox 5.3 were used to evaluate virtualization performance. The experimental environment was conducted on server Cisco UCS B200 M4 which was the host machine and the virtual environment that is encapsulated within the physical layer which hosts the guest virtual machines consisting of virtual hardware, Guest OSs, and third-party applications. The host server consists of virtual machines with one operating system, CentOS 7 64 bit. For performance evaluation purposes, each guest operating system was configured and allocated the same amount of virtual system resources. Various Workload/benchmarking tools were used for Network, CPU, Memory as well as Disk performance, namely; Iperf, Unibench, Ramspeed, and IOzone, respectively. In the case of Iozone, VMware was more than twice as fast as Proxmox. Although CPU utilization in Proxmox was not noticeably affected, considerably less CPU utilization was observed in VMware. While testing the memory performance with ramspeed, VMware performs 16 to 26% better than Proxmox. In the case of writing, VMware observed 31 to 51% better than Proxmox. In a network, it was observed that the performance on Proxmox was very close to the level of bare metal setup. The results of the performance tests show that the additional operations required by virtualization can be confirmed utilizing test programs. The number of additional operations and their type influence specifically to performance as overhead. In memory and disk areas, where the virtualization procedure was clear, the test outcomes demonstrate that the measure of overhead is little. Processor and network virtualization, then again, was more perplexing. Hence the overhead is more significant. At the point when the overall performance of a virtual machine running in VMware ESXi Server is contrasted with a conventional system, the virtualization causes approximately an increase of 33% in performance.Because of the difficulty in providing optimal real system configurations, workload/benchmarks could provide close to real application systems for better results. The tests demonstrate that virtualization depends immensely on the host system and the virtualization software. Given the tests, both VMware ESXi Server and Proxmox are capable of providing Optimal performance.

volving technology, has kept drawing a significant attention from both the computing industry and academia for nearly a decade.

Cloud computing has grown from being a business concept to one of the fastest growing segments of modern ICT industry. Cloud computing are addressing many issues emerged by the globalization in terms of the ever faster pace of growth, shorter product life cycles, increased complexity of systems and higher investment needs. Cloud computing is enetrating all sectors of business applications and has influenced the whole IT industry. The business model has grown to be an alternative to traditional on-premise systems, where traditional environment, applications and additional IT infrastructure is maintained in-house within the organization. However, organizations are still reluctant to deploy their business in the cloud. There are many concerns regarding cloud computing services and despite all its advantages, cloud adoption is still very low at an organizational landscape. Hence, this master thesis aims to investigate what the drawbacks regarding a transition from an on-premise system to a cloud computing service are and how these relate to factors that influence the decision of adoption. Furthermore, this study will investigate how cloud service providers can develop a pro-active approach to manage the main drawbacks of cloud adoption. In order to fulfill the aim of the study, empirical research in form of data collection of  onducted interviews were carried out. The results of the study identified security and perceived loss of control as the main drawbacks in the transition from an on-premise system to a cloud computing service. Since these findings could be described as foremost technological and attitudinal, the thesis contributes to practitioners in terms of implications of communicating and educating customers and adherence to industry standards and certifications as important factors to address. Lastly, this thesis identified lack of understanding for cloud computing as a result of poor information, indicating for further research within this area.

Yes / By 2020, next generation (5G) cellular networks are expected to support a 1000 fold traffic increase. To meet such traffic demands, Base Station (BS) densification through small cells are deployed. However, BSs are costly and consume over half of the cellular network energy. Meanwhile, Cloud Radio Access Networks (C-RAN) has been proposed as an energy efficient architecture that leverage cloud computing technology where baseband processing is performed in the cloud. With such an arrangement, more energy gains can be acquired through statistical multiplexing by reducing the number of BBUs used. This paper proposes a green Intelligent Traffic and Resource Elastic Energy (iTREE) scheme for C-RAN. In iTREE, BBUs are reduced by matching the right amount of baseband processing with traffic load. This is a bin packing problem where items (BS aggregate traffic) are to be packed into bins (BBUs) such that the number of bins used are minimized. Idle BBUs can then be switched off to save energy. Simulation results show that iTREE can reduce BBUs by up to 97% during off peak and 66% at peak times with RAN power reductions of up to 27% and 18% respectively compared with conventional deployments.