The thesis deals with the field called Integration as a Service (IaaS), where the essence is to provide integration functionality in cloud computing environment. The goal of the thesis is to research from the publicly available sources, whether capabilities of the services in the field of application integration, which are labeled as integration as a service, match by the market leaders functionality of middleware. In order to achieve the goal Integration as a Service is defined and the key capabilities for these kinds of services are found. It's followed with identification of the main providers and analysis of their services. Capabilities, which should be achieved by the IaaS services, are based upon the definitions and characteristics of middleware, cloud computing and services in general. Together they create a standard, which can be used to evaluate these services. That's utilized in the practical part, where the analysis of the particular IaaS services takes place. Also the questions regarding expected capabilities of the services, their fulfillment and the level of publicly available information about the services are answered in this part.

The emergence of large-scale Internet services coupled with the evolution of computing technologies such as distributed systems, parallel computing, utility computing, grid, and virtualization has fueled a movement toward a new resource provisioning paradigm called cloud computing. The main appeal of cloud computing lies in its ability to provide a shared pool of infinitely scalable computing resources for cloud services, which can be quickly provisioned and released on-demand with minimal effort. The rapidly growing interest in cloud computing from both the public and industry together with the rapid expansion in scale and complexity of cloud computing resources and the services hosted on them have made monitoring, controlling, and provisioning cloud computing resources at runtime into a very challenging and complex task. This thesis investigates algorithms, models and techniques for autonomously monitoring, controlling, and provisioning the various resources required to meet services’ performance requirements and account for their resource usage. Quota management mechanisms are essential for controlling distributed shared resources so that services do not exceed their allocated or paid-for budget. Appropriate cloud-wide monitoring and controlling of quotas must be exercised to avoid over- or under-provisioning of resources. To this end, this thesis presents new distributed algorithms that efficiently manage quotas for services running across distributed nodes. Determining the optimal amount of resources to meet services’ performance requirements is a key task in cloud computing. However, this task is extremely challenging due to multi-faceted issues such as the dynamic nature of cloud environments, the need for supporting heterogeneous services with different performance requirements, the unpredictable nature of services’ workloads, the non-triviality of mapping performance measurements into resources, and resource shortages. Models and techniques that can predict the optimal amount of resources needed to meet service performance requirements at runtime irrespective of variations in workloads are proposed. Moreover, different service differentiation schemes are proposed for managing temporary resource shortages due to, e.g., flash crowds or hardware failures. In addition, the resources used by services must be accounted for in order to properly bill customers. Thus, monitoring data for running services should be collected and aggregated to maintain a single global state of the system that can be used to generate a single bill for each customer. However, collecting and aggregating such data across geographical distributed locations is challenging because the management task itself may consume significant computing and network resources unless done with care. A consistency and synchronization mechanism that can alleviate this task is proposed.

The increasing needs for computational power in areas such as weather simulation, genomics or Internet applications have led to sharing of geographically distributed and heterogeneous resources from commercial data centers and scientific institutions. Research in the areas of utility, grid and cloud computing, together with improvements in network and hardware virtualization has resulted in methods to locate and use resources to rapidly provision virtual environments in a flexible manner, while lowering costs for consumers and providers. However, there is still a lack of methodologies to enable efficient and seamless sharing of resources among institutions. In this work, we concentrate in the problem of executing parallel scientific applications across distributed resources belonging to separate organizations. Our approach can be divided in three main points. First, we define and implement an interoperable grid protocol to distribute job workloads among partners with different middleware and execution resources. Second, we research and implement different policies for virtual resource provisioning and job-to-resource allocation, taking advantage of their cooperation to improve execution cost and performance. Third, we explore the consequences of on-demand provisioning and allocation in the problem of site-selection for the execution of parallel workloads, and propose new strategies to reduce job slowdown and overall cost.

Cloud computing represents a distributed computing paradigm that gained notoriety due to its properties related to on-demand elastic and dynamic resource provisioning. These characteristics are highly desirable for the execution of workflows, in particular scientific workflows that required a great amount of computing resources and that handle large-scale data. One of the main questions in this sense is how to manage resources of one or more cloud infrastructures to execute workflows while optimizing resource utilization and minimizing the total duration of the execution of tasks (makespan). The more complex the infrastructure and the tasks to be executed are, the higher the risk of incorrectly estimating the amount of resources to be assigned to each task, leading to both performance and monetary costs. Scenarios which are inherently more complex, such as hybrid and multiclouds, rarely are considered by existing resource management solutions. Moreover, a thorough research of relevant related work revealed that most of the solutions do not address data-intensive workflows, a characteristic that is increasingly evident for modern scientific workflows. In this sense, this proposal presents MPSF, the Multiphase Proactive Scheduling Framework, a cloud resource management solution based on multiple scheduling phases that continuously assess the system to optimize resource utilization and task distribution. MPSF defines models to describe and characterize workflows and resources. MPSF also defines performance and reliability models to improve load distribution among nodes and to mitigate the effects of performance fluctuations and potential failures that might occur in the system. Finally, MPSF defines a framework and an architecture to integrate all these components and deliver a solution that can be implemented and tested in real applications. Experimental results show that MPSF is able to predict with much better accuracy the duration of workflows and workflow phases, as well as providing performance gains compared to greedy approaches. / A computação em nuvem representa um paradigma de computação distribuída que ganhoudestaque devido a aspectos relacionados à obtenção de recursos sob demanda de modo elástico e dinâmico. Estas características são consideravelmente desejáveis para a execução de tarefas relacionadas a fluxos de trabalho científicos, que exigem grande quantidade de recursos computacionais e grande fluxo de dados. Uma das principais questões neste sentido é como gerenciar os recursos de uma ou mais infraestruturas de nuvem para execução de fluxos de trabalho de modo a otimizar a utilização destes recursos e minimizar o tempo total de execução das tarefas. Quanto mais complexa a infraestrutura e as tarefas a serem executadas, maior o risco de estimar incorretamente a quantidade de recursos destinada para cada tarefa, levando a prejuízos não só em termos de tempo de execução como também financeiros. Cenários inerentemente mais complexos como nuvens híbridas e múltiplas nuvens raramente são considerados em soluções existentes de gerenciamento de recursos para nuvens. Além destes fatores, a maioria das soluções não oferece mecanismos claros para tratar de fluxos de trabalho com alta intensidade de dados, característica cada vez mais proeminente em fluxos de trabalho moderno. Neste sentido, esta proposta apresenta MPSF, uma solução de gerenciamento de recursos baseada em múltiplas fases de gerenciamento baseadas em mecanismos dinâmicos de alocação de tarefas. MPSF define modelos para descrever e caracterizar fluxos de trabalho e recursos de modo a suportar cenários simples e complexos, como nuvens híbridas e nuvens integradas. MPSF também define modelos de desempenho e confiabilidade para melhor distribuir a carga e para combater os efeitos de possíveis falhas que possam ocorrer no sistema. Por fim, MPSF define um arcabouço e um arquitetura que integra todos estes componentes de modo a definir uma solução que possa ser implementada e utilizada em cenários reais. Testes experimentais indicam que MPSF não só é capaz de prever com maior precisão a duração da execução de tarefas, como também consegue otimizar a execução das mesmas, especialmente para tarefas que demandam alto poder computacional e alta quantidade de dados.

The data analytics process has many time-consuming steps. Combining data that sits in a relational database warehouse into a single relation while aggregating important information in a meaningful way and preserving relationships across relations, is complex and time-consuming. This step is exceptionally important as many machine learning algorithms require a single file format as an input (e.g. supervised and unsupervised learning, feature representation and feature learning, etc.). An analyst is required to manually combine relations while generating new, more impactful information points from data during the feature synthesis phase of the feature engineering process that precedes machine learning. Furthermore, the entire process is complicated by Big Data factors such as processing power and distributed data storage. There is an open-source package, Featuretools, that uses an innovative algorithm called Deep Feature Synthesis to accelerate the feature engineering step. However, when working with Big Data, there are two major limitations. The first is the curse of modularity - Featuretools stores data in-memory to process it and thus, if data is large, it requires a processing unit with a large memory. Secondly, the package is dependent on data stored in a Pandas DataFrame. This makes the use of Featuretools with Big Data tools such as Apache Spark, a challenge. This dissertation aims to examine the viability and effectiveness of using Featuretools for feature synthesis with Big Data on the cloud computing platform, AWS. Exploring the impact of generated features is a critical first step in solving any data analytics problem. If this can be automated in a distributed Big Data environment with a reasonable investment of time and funds, data analytics exercises will benefit considerably. In this dissertation, a framework for automated feature synthesis with Big Data is proposed and an experiment conducted to examine its viability. Using this framework, an infrastructure was built to support the process of feature synthesis on AWS that made use of S3 storage buckets, Elastic Cloud Computing services, and an Elastic MapReduce cluster. A dataset of 95 million customers, 34 thousand fraud cases and 5.5 million transactions across three different relations was then loaded into the distributed relational database on the platform. The infrastructure was used to show how the dataset could be prepared to represent a business problem, and Featuretools used to generate a single feature matrix suitable for inclusion in a machine learning pipeline. The results show that the approach was viable. The feature matrix produced 75 features from 12 input variables and was time efficient with a total end-to-end run time of 3.5 hours and a cost of approximately R 814 (approximately $52). The framework can be applied to a different set of data and allows the analysts to experiment on a small section of the data until a final feature set is decided. They are able to easily scale the feature matrix to the full dataset. This ability to automate feature synthesis, iterate and scale up, will save time in the analytics process while providing a richer feature set for better machine learning results.

To support increasingly sophisticated sensors and resource-hungry applications with the current-used Lithium-based batteries and to augment mobile computing power further, the concept of the Cloudlet-based offloading is proposed which enables to migrate part of application computing tasks from battery-limited low-capacity mobile elements to the local edge. Such Cloudlet-based offloading technologies extend the provisioning of computing and storage capabilities from remote Cloud Data Centers to the proximity of end users via heterogeneous networks. However, Cloudlet-based offloading is required to coordinate among User Equipment, inter-Cloudlet nodes and remote Cloud Data Centers, which emerges new challenges and issues regarding how to enable Cloudlet-based offloading in the context of mobile edge environment and how to achieve execution- and energy-efficient offloading allocation under limited available resources. In this dissertation, a Cloudlet-based Mobile Cloud offloading prototype is first proposed. A mechanism for handling diverse computing resources is described; by adopting it, idle public resources can be easily configured as additional computing capabilities in the virtual resource pool. A fast deployment model is built to relieve the migration and installation cost when adapting the platform. An energy-saving strategy is utilized to reduce the consumption of computing resources. Security components are implemented to protect sensitive information and block malicious attacks in the cloud. Concerning the limited processing capability on the edge, a task-centric energy-aware Cloudlet-based Mobile Cloud model is formulated. A Cloudlet task-based offloading mechanism is proposed to achieve energy-aware offloading resource preparation and scheduling on the Cloudlet. A Cloud task-centric scheduling algorithm is presented for the green collaborative offloading processing between Cloudlet and remote Cloud. Considering the dynamic and heterogeneity of the offloading environment, a hybrid offloading model to solve the heterogeneous resource-constraint offloading issues on the dynamic Cloudlets. A queue-based offloading framework is developed to formulate and analyze the mixed migration-based and partition-based offloading behaviours on the Cloudlet. The execution and energy-aware heterogeneous offloading resource allocation problem is formalized and solved. A time series-based load prediction model is designed on the Cloudlet to achieve fine-grain proactive resource allocation. Regarding the mobility of User Equipment and the diverse priority of offloading tasks, an edge-based mobility-aware offloading model is modeled to solve the intra-Cloudlet offloading scheduling issue and inter-Cloudlet load-aware heterogeneous resource allocation issue. A priority-based queueing model is designed to formulate the intra-Cloudlet mobility-aware offloading scheduling problem, resolved by a heuristic solution. The energy-aware inter-Cloudlet resource selection procedure is formalized in a mobility-aware multi-site resource allocation model, which is further solved by lightweight dynamic load balancing.

Durante los últimos diez años, se han desarrollado diferentes tecnologías que aún se desconocen, ya sea por falta de interés o por falta de información de la misma, dado su alto costo de implementación o una serie de factores. Sin embargo, a medida que pasan los años, estas tecnologías se están implementando a pasos acelerados. Las cuatro principales macro tecnologías que engloban a todos estos productos son: Información, Social, Mobile y Cloud. Estos, a su vez, incluyen teléfonos y casas inteligentes que denotan una gran cantidad de transferencia de información y presencia en los cuatro principales cuadrantes. La tecnología evoluciona hasta un determinado momento en el que parece ya no avanzar más. A su vez, las aplicaciones parecen haber dado todo su potencial en los Smartphones; sin embargo, aparecen los diferentes equipos que revolucionan al mercado tales como los productos de la empresa Nest que combinan la información con el hardware y a su vez reduce sustantivamente el tamaño de los dispositivos. Es debido a este acelerado avance que diversos rubros ligados a la tecnología, en su mayoría el hogar y las personas en general, no se toman el tiempo de realizar una investigación minuciosa de estos avances tecnológicos, lo que en un futuro conlleva una falta de conocimiento en las herramientas orientadas a su línea de investigación tecnológica. Por ello, es de suma importancia, conocer estas nuevas tecnologías para estar a la vanguardia de los últimos desarrollos y avances, ya que con mayor frecuencia aparecen en la vida cotidiana y en cada momento del día. En el presente proyecto se propone un modelo de solución tecnológica orientado al ambiente de la cocina y la experiencia del usuario, que permita tener un mayor conocimiento de cómo estas tecnologías son utilizadas como oportunidades de mejora y obtener el mayor beneficio de ellas en favor de su comodidad y seguridad. / During the past ten years, it has developed different technologies that are still unknown, either for lack of interest or lack of information of the same, given the high cost of implementation or a number of factors. However, as the years pass, these technologies are being implemented at an accelerated pace. The four main macro technologies that encompass all of these products are: Information, Social, Mobile and Cloud. These, in turn, include phones and smart homes that show a lot of information transfer and presence in the four main quadrants. Technology evolves to a certain point where it seems to go no further. In turn, the applications seem to have given their full potential in smartphones; however, the different teams that revolutionize the market such as company products Nest which combine information with hardware and in turn substantially reduces the size of the devices appear. It is due to this accelerated progress various areas related to technology, most home and people in general, do not take the time to conduct a thorough investigation of these technological advances, which in the future leads to a lack of knowledge tools in their line-oriented technology research. It is therefore extremely important to know these new technologies to be at the forefront of the latest developments and progress, and appear most often in everyday life and every moment of the day. The present project intends a model of technological solution oriented kitchen environment and user experience that allows for a better understanding of how these technologies are used as opportunities for improvement and get the most benefit from them in favor of proposed its comfort and safety. / Tesis

Cloud computing has provided many services to potential consumers, one of these services being the provision of network functions using virtualization. Network Function Virtualization is a new technology that aims to improve the way we consume network services. Legacy networking solutions are different because consumers must buy and install various hardware equipment. In NFV, networks are provided to users as a software as a service (SaaS). Implementing NFV comes with many benefits, including faster module development for network functions, more rapid deployment, enhancement of the network on cloud infrastructures, and lowering the overall cost of having a network system. All these benefits can be achieved in NFV by turning physical network functions into Virtual Network Functions (VNFs). However, since this technology is still a new network paradigm, integrating this virtual environment into a legacy environment or even moving all together into NFV reflects on the complexity of adopting the NFV system. Also, a network service could be composed of several components that are provided by different service providers; this also increases the complexity and heterogeneity of the system. We apply abstract architectural modeling to describe and analyze the NFV architecture. We use architectural patterns to build a flexible NFV architecture to build a Reference Architecture (RA) for NFV that describe the system and how it works. RAs are proven to be a powerful solution to abstract complex systems that lacks semantics. Having an RA for NFV helps us understand the system and how it functions. It also helps us to expose the possible vulnerabilities that may lead to threats toward the system. In the future, this RA could be enhanced into SRA by adding misuse and security patterns for it to cover potential threats and vulnerabilities in the system. Our audiences are system designers, system architects, and security professionals who are interested in building a secure NFV system. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Network security is a complex field that is handled by security professionals who need certain expertise and experience to configure security systems. With the ever increasing size of the networks, managing them is going to be a daunting task. What kind of solution can be used to generate effective security configurations by both security professionals and nonprofessionals alike? In this thesis, a web tool is developed to simplify the process of configuring security systems by translating direct human language input into meaningful, working security rules. These human language inputs yield the security rules that the individual wants to implement in their network. The human language input can be as simple as, "Block Facebook to my son's PC". This tool will translate these inputs into specific security rules and install the translated rules into security equipment such as virtualized Cisco FWSM network firewall, Netfilter host-based firewall, and Snort Network Intrusion Detection. This tool is implemented and tested in both a traditional network and a cloud environment. One thousand input policies were collected from various users such as staff from UNT departments' and health science, including individuals with network security background as well as students with a non-computer science background to analyze the tool's performance. The tool is tested for its accuracy (91%) in generating a security rule. It is also tested for accuracy of the translated rule (86%) compared to a standard rule written by security professionals. Nevertheless, the network security tool built has shown promise to both experienced and inexperienced people in network security field by simplifying the provisioning process to result in accurate and effective network security rules.

This master’s thesis deals with design of the monitoring solution for the Power BI service. The thesis is divided into theoretical, analytical and design sections. In the theoretical part describes the theoretical fundamentals, used technologies and analytical tools. The analytical part analyzes the company Intelligent Technologies, competitive solutions and data sources for the design part. The design part proposes its own solution for monitoring the Power BI service, including the costs and benefits of the proposed solution.