This thesis was motivated by Roberts (2010) who found that 63% of SMEs in South Africa do not make it past second year of operation. To expand further on this problem, we reviewed literature to understand key business challenges experienced by SMEs in South Africa which contribute to this high failure rate. The challenges include red tape, labour legislation, lack of skills, lack of innovation, impact of crime, and lack of funds. The research project aimed to answer a key question: “How can information technology, in the form of Cloud Computing be used to address the challenges faced by small and medium businesses in South Africa?” To answer this question, data was collected from 265 SME companies and quantitatively analysed. It is important to note that the profile of SMEs targeted in this study are those that employed fewer than 200 employees, with a turnover of not less than 26 million rand per annum, and registered with South African Revenue Services (SARS) and also with the Companies and Intellectual Property Commission (CIPC) of South Africa. Over 60% of the firms that responded to the survey were in business for more than 10 years which means we are mainly dealing with data from businesses that have past the survivalist stage and are matured businesses. These are businesses that can share their experiences and challenges they faced throughout their journey. The profile of SMEs in this study should not be confused with that of Very Small Medium Enterprise Businesses. The questionnaire was designed to address four themes being the Demographic profile, SME Business Environment, Threat of Survival, and lastly Technology Adoption. Key finding in this research is that 60% of the panellists stated that red tape is the overriding challenge that small businesses contend with. 67% of the panellists confirmed that they have not invested in their businesses in the past year; and 53% stated that they have not applied for finance from the bank for fear of being rejected. Only 30% of the SME market were found to use enterprise resource planning (ERP) and 62% do not have their own IT department. Of great concern is that 65% of the panellists have experienced server down time at least once in the past year. Inability to predict the rising IT costs in a firm has been cited as the main concern when running IT on premise. The cost predictability finding was also discovered to be a benefit enjoyed by the SMEs who use Cloud Computing. The conclusion is that there is a relationship between Cloud Computing, Small and Medium Enterprise businesses and the challenges they face in their business environment. To address the identified business challenges, technology adoption studies by Gumbi & Mnkandla (2015), Carcary, Doherty & Conway (2014), Lacovou et al (1995), Mohlomeane & Ruxwana (2014), Kshetri (2010), BMI Research (2018), Conway & Curry (2012), Li, Zhao & Yu (2015), Wernefeldt (1985), Schindehuitte & Morris (2001), Tornatzy & Flesher (1991) were reviewed. From these publications, the Technology, Organisational and Environmental (TOE) was found to be relevant and of interest for use in answering the main research question. This study developed the Cloud Adoption Framework which is the anchor of all SME challenges. Key study contribution is that the TOE model, which is predominantly used to understand the determinants of technology adoption like various industry applications, infrastructure innovations etc., are now used to address specific challenges that have contributed in the high failure rate of SME business. This is the first-time TOE model has been used to align with key SME challenges that contribute to firms’ failure. Specific technology across Software, Infrastructure and Platform services models are recommended for use by SMEs to ensure challenges are mitigated and improve the chances of survival for SMEs operating in South Africa. By following the recommended Cloud Adoption Framework, SMEs should be able to navigate the complexities brought about by the tough operating environment and also the technologies available to address those challenges. All six challenges have solutions in Cloud Computing and SMEs are educated on these solutions and also how to access these on a pay as you use model of consumption. / Business Management / D.B.L.

Organizational subunits such as marketing, sales, human resources, and customer service invest in software as a service (SaaS) as a means to reduce information technology costs, speed time to market, gain access to new technologies, and improve application support and maintenance. For these reasons, SaaS has been characterized as a form of outsourcing, and one in which the internal IT function is losing relevancy because contracts are being executed between external application service providers and the affected subunits directly without IT oversight. Here we argue that SaaS is not outsourcing as it has been traditionally envisioned and enacted, and that through the generation of four types of functional slack it has demonstrated the ability to result in higher levels of IT innovation in support of a digital business strategy. A redistribution of IT resources from efficiency to innovation as a result of SaaS adoption was found that prescribed movement toward an equilibrium of ambidexterity between exploitative and exploratory activities. This research has established a number of firsts: 1) explored the production and combination of multidimensional slack, concentrated at a functional level; 2) demonstrated a previously disconfirmed positive relationship between IT outsourcing and innovation; 3) confirmed the feasibility of a theorized positive relationship between outsourcing and ambidexterity, and 4) discovered a new pathway within the realm of digital business strategy between a key external trend and an internal organizational shift of roles, responsibilities, and knowledge patterns. Further, our findings suggest that IT ambidexterity may be a better construct for investigating the impact of IT on firm performance than traditional measures of IT performance.

AN ABSTRACT OF THE THESIS OF Abdulaziz M. AlNowiser, for the Master of Science degree in Computer Science, presented on November 1, 2013, at Southern Illinois University Carbondale. TITLE: TOWARD ENERGY-EFFICIENT SCHEDULING USING WEIGHTED ROUND- ROBIN AND VM REUSE MAJOR PROFESSOR: Dr. Michelle M. Zhu In recent years, the rapid evolving Cloud Computing technologies multiply challenges such as minimizing power consumption and meeting Quality-of-Services (QoS) requirements in the presence of heavy workloads from a large number of users using shared computing resources. Powering a middle-sized data center normally consumes 80,000kW power every year and computer servers consume around .5% of the global power [1]. Statistics for 5000 production servers over a six-month period show that only 10-50% of the total capacity has been effectively used, and a large portion of the resources is actually wasted. In order to address the skyrocket energy cost from the high level resource management aspect, we propose an energy efficient job scheduling approach based on a modified version of Weighted Round Robin scheduler that incorporates VMs reuse and live VM migration without compromising the Service Level Agreement (SLA). The Weighted Round Robin scheduler can monitor the running VMs status for possible VM sharing for job consolidation or migration. In addition, the VMs utilization rate is observed to start live migration from the over-utilizing Processing Element (PE) to under-utilized PEs or to the hibernated PEs by sending WOL (Wake-On-LAN) signal to activate them. The simulation experiments are conducted under the CloudReports environment based on open source CloudSim simulator. The comparisons with other similar scheduling algorithms demonstrate that our enhanced Weighted Round Robin algorithm (EWRR) can achieve considerable better performance in terms of energy consumption and resource utilization rate.

In the recent years, cloud services are dominating the internet world. Streaming video content is drastically increased. This trend created a way for the cloud gaming industry. Gaming Anywhere is one such an open source cloud gaming system designed specifically for gaming users to experience high-quality gaming while eliminating the burden of upgrading hardware or software whenever the gaming system becomes outdated. Gaming Anywhere client can stream the desired game from a remote server with high-quality resolution and fps. Gaming Anywhere launches almost all type of games as per the request of the client. In this paper, the performance of a Gaming Anywhere server is evaluated in a virtual windows environment. Performance is categorically divided into two types. One is server metrics and the other is power consumption. Server metrics deals with CPU utilization, GPU utilization and multi-player capability. Power consumption deals with the CPU power usage of a virtual machine. The main aim of this paper is to conduct measurement studies on Gaming Anywhere server in two virtual implementation kits, VMware and VirtualBox, using monitoring tools. The analysis of the outcome is evaluated against the Gaming Anywhere physical server.

A indústria do software dispõe de características muito particulares, como a inexistência de muitos competidores em cada segmento, e ainda assim, ser um mercado caracterizado por muitas inovações. A mais nova, a nuvem, permite a entrada de muitas empresas que ingressam no mercado de software, competindo com as empresas tradicionais de software em microcomputadores. Com o intuito de analisar este mercado, este trabalho tem por objetivo analisar os determinantes e as características estratégicas que levam as empresas desenvolvedoras de software a adoção da computação em nuvem. Para a realização do estudo, inicia-se com uma revisão teórica sobre competição e sua aplicação na indústria da computação. Posteriormente, faz-se uma apresentação da indústria da computação, comparando, em cada período, movimentos competitivos dos atores em referência à literatura revisada. Em seguida, analisam-se os incentivos que motivam o movimento das fabricantes de software para a adoção da computação em nuvem, recorrendo a análises de viabilidade, como comparações de vendas de computadores versus celulares, utilização da internet por dispositivos móveis, índice de conectividade por país, e comparação de valor de mercado entre empresas de software on-premise e empresas de software em nuvem. O trabalho ainda faz menção a efeitos econômicos e sociais da computação em nuvem, como capacidade de sua adoção também por outras indústrias, que agora fazem uso de dispositivos que se conectam à internet para transmissão de dados, coletando informações para aperfeiçoar seus produtos. De uma maneira geral, o estudo concluiu que a indústria da computação tem características de mercados monopolísticos, com alta importância na externalidade de rede e fortes barreiras de entrada, atrelado a custos de distribuição e reprodução muito baixos. Isso explica ascensões muito rápidas de empresas e também rápidas maturidades de produtos e consequentemente, o declínio nas vendas. Deste declínio, surge a necessidade de uma mudança de tecnologia para que as vendas voltem a crescer. Assim, o trabalho conclui que o mercado está em ascensão com o modelo de computação em nuvem em virtude de muitas oportunidades, como o crescente uso de dispositivos móveis, que agora podem conectar-se à internet e disfrutar de softwares para incrementar sua usabilidade. Dessas oportunidades, concluímos ainda que a computação em nuvem fará com que o desenvolvimento do software não seja mais privilégio de empresas com este fim, mas outras indústrias também entrarão neste mercado. / The software industry has characteristics very particular, such the non-existence of many competitors in each segment, and despite that, it is a market characterized for presenting many innovations. The newest one, the cloud, is allowing the entrance of many companies in the software market, competing with traditional microcomputer software companies. With the idea to analyze this market, this study aims to analyze the incentives leading software providers to change their products and begin to offer them in cloud computing models. The study begins with a theoretical review of competition and its application to the information technology (IT) industry. Later, we introduce the IT industry, comparing competitive movements of the actors in different periods, in reference to the literature that we reviewed. Then, we analyze the incentives that stimulate software providers to adopt cloud computing by using feasibility studies, such as sales comparisons of computers vs. cell phones, Internet usage by mobile devices, global connectivity index, and a comparison of the market value growth between on-premise software companies and cloud software companies. The study also addresses economic and social effects of cloud computing, such as the ability of other industries to adopt cloud computing in order to create value in their products with the use of Internet-connected devices, which are able to transmit and collect data. Overall, the study found that the IT industry has peculiar characteristics, such as presence in monopolistic markets, high level of importance in the network externalities, and high barriers to entry, plus very low distribution and reproduction costs. This explains how companies grow and achieve a level of product maturity fast, which leads to a decrease in sales. Out of this decrease comes the need for a change of technology, so that sales will grow again. Therefore, the study concludes that the cloud computing market is ascending due to many opportunities, such as the growing use of mobile devices, which can connect to the Internet and increase its usage through software. By analyzing these opportunities, the study also concludes that cloud computing will make software development no longer a privilege of software companies, since other industries will also join the market.

Nos últimos anos, observamos o alvorecer de uma grande quantidade de aplicações que utilizam robôs autônomos. Para que um robô seja considerado verdadeiramente autônomo, é primordial que ele possua a capacidade de aprender sobre o ambiente no qual opera. Métodos de SLAM (Localização e Mapeamento Simultâneos) constroem um mapa do ambiente por onde o robô trafega ao mesmo tempo em que estimam a trajetória correta do robô. No entanto, para obter um mapa completo do ambiente de forma autônoma é preciso guiar o robô por todo o ambiente, o que é feito no problema de exploração. Câmeras são sensores baratos que podem ser utilizadas para a construção de mapas 3D. Porém, o problema de exploração em mapas gerados por métodos de SLAM monocular, i.e. que extraem informações de uma única câmera, ainda é um problema em aberto, pois tais métodos geram mapas esparsos ou semi-densos, que são inadequados para navegação e exploração. Para tal situação, é necessário desenvolver métodos de exploração capazes de lidar com a limitação das câmeras e com a falta de informação nos mapas gerados por SLAMs monoculares. Propõe-se uma estratégia de exploração que utilize mapas volumétricos locais, gerados através das linhas de visão, permitindo que o robô navegue em segurança. Nestes mapas locais, são definidos objetivos que levem o robô a explorar o ambiente desviando de obstáculos. A abordagem proposta visa responder a questão fundamental em exploração: "Para onde ir?". Além disso, busca determinar corretamente quando o ambiente está suficientemente explorado e a exploração deve parar. A abordagem proposta é avaliada através de experimentos em um ambiente simples (i.e. apenas uma sala) e em um ambiente compostos por diversas salas. / In recent years, we have seen the dawn of a large number of applications that use autonomous robots. For a robot to be considered truly autonomous, it is primordial that it has the ability to learn about the environment in which it operates. SLAM (Simultaneous Location and Mapping) methods build a map of the environment while estimating the robot’s correct trajectory. However, to autonomously obtain a complete map of the environment, it is necessary to guide the robot throughout the environment, which is done in the exploration problem. Cameras are inexpensive sensors that can be used for building 3D maps. However, the exploration problem in maps generated by monocular SLAM methods (i.e. that extract information from a single camera) is still an open problem, since such methods generate sparse or semi-dense maps that are ill-suitable for navigation and exploration. For such a situation, it is necessary to develop exploration methods capable of dealing with the limitation of the cameras and the lack of information in the maps generated by monocular SLAMs. We proposes an exploration strategy that uses local volumetric maps, generated using the lines of sight, allowing the robot to safely navigate. In these local maps, objectives are defined to lead the robot to explore the environment while avoiding obstacles. The proposed approach aims to answer the fundamental question in exploration: "Where to go?". In addition, it seeks to determine correctly when the environment is sufficiently explored and the exploration must stop. The effectiveness of the proposed approach is evaluated in experiments on single and multi-room environments.

Cloud computing is known for its on-demand service provisioning and has now become mainstream. Many businesses as well as individuals are using cloud services on a daily basis. There is a big variety of services that ranges from the provision of computing resources to services such as productivity suites and social networks. The nature of these services varies heavily in terms of what kind of information is being out-sourced to the cloud provider. Often, that data is sensitive, for instance when PII is being shared by an individual. Also, businesses that move (parts of) their processes to the cloud are actively participating in a major paradigm shift from having data on-premise to transfering data to a third-party provider. However, many new challenges come along with this trend, which are closely tied to the loss of control over data. When moving to the cloud, direct control over geographical storage location, who has access to it and how it is shared and processed is given up. Because of this loss of control, cloud customers have to trust cloud providers that they treat their data in an appropriate and responsible way. Cloud audits can be used to check how data has been processed in the cloud (i.e., by whom, for what purpose) and whether or not this happened in compliance with what has been defined in agreed-upon privacy and data storage, usage and maintenance (i.e., data handling) policies. This way, a cloud customer can regain some of the control he has given up by moving to the cloud. In this thesis, accountability audits are presented as a way to strengthen trust in cloud computing by providing assurance about the processing of data in the cloud according to data handling and privacy policies. In cloud accountability audits, various distributed evidence sources need to be considered. The research presented in this thesis discusses the use of various heterogeous evidence sources on all cloud layers. This way, a complete picture of the actual data handling practices that is based on hard facts can be presented to the cloud consumer. Furthermore, this strengthens transparency of data processing in the cloud, which can lead to improved trust in cloud providers, if they choose to adopt these mechanisms in order to assure their customers that their data is being handled according to their expectations. The system presented in this thesis enables continuous auditing of a cloud provider's adherence to data handling policies in an automated way that shortens audit intervals and that is based on evidence that is produced by cloud subsystems. An important aspect of many cloud offerings is the combination of multiple distinct cloud services that are offered by independent providers. Data is thereby freuqently exchanged between the cloud providers. This also includes trans-border flows of data, where one provider may be required to adhere to more strict data protection requirements than the others. The system presented in this thesis addresses such scenarios by enabling the collection of evidence at providers and evaluating it during audits. Securing evidence quickly becomes a challenge in the system design, when information that is needed for the audit is deemed sensitive or confidential. This means that securing the evidence at-rest as well as in-transit is of utmost importance, in order not to introduce a new liability by building an insecure data heap. This research presents the identification of security and privacy protection requirements alongside proposed solutions that enable the development of an architecture for secure, automated, policy-driven and evidence-based accountability audits.

Dans cette thèse, nous présentons notre travail de recherche dans le domaine du calcul haute performance en mécanique des fluides (CFD) pour architectures de type cluster et cloud. De manière générale, nous nous proposons de développer un solveur efficace, appelé ADAPT, pour la résolution de problèmes de CFD selon une vue classique correspondant à des développements en MPI et selon une vue qui nous amène à représenter ADAPT comme un graphe de tâches destinées à être ordonnancées sur une plateforme de type cloud computing. Comme première contribution, nous proposons une parallélisation de l’équation de diffusion-convection couplée àun système linéaire en 2D et en 3D à l’aide de MPI. Une parallélisation à deux niveaux est utilisée dans notre implémentation pour exploiter au mieux les capacités des machines multi-coeurs. Nous obtenons une distribution équilibrée de la charge de calcul en utilisant la décomposition du domaine à l’aide de METIS, ainsi qu’une résolution pertinente de notre système linéaire creux de très grande taille en utilisant le solveur parallèle MUMPS (Solveur MUltifrontal Massivement Parallèle). Notre deuxième contribution illustre comment imaginer la plateforme ADAPT, telle que représentée dans la premièrecontribution, comme un service. Nous transformons le framework ADAPT (en fait, une partie du framework)en DAG (Direct Acyclic Graph) pour le voir comme un workflow scientifique. Ensuite, nous introduisons de nouvelles politiques à l’intérieur du moteur de workflow RedisDG, afin de planifier les tâches du DAG, de manière opportuniste.Nous introduisons dans RedisDG la possibilité de travailler avec des machines dynamiques (elles peuvent quitter ou entrer dans le système de calcul comme elles veulent) et une approche multi-critères pour décider de la “meilleure”machine à choisir afin d’exécuter une tâche. Des expériences sont menées sur le workflow ADAPT pour illustrer l’efficacité de l’ordonnancement et des décisions d’ordonnancement dans le nouveau RedisDG. / In this thesis, we present our research work in the field of high performance computing in fluid mechanics (CFD) for cluster and cloud architectures. In general, we propose to develop an efficient solver, called ADAPT, for problemsolving of CFDs in a classic view corresponding to developments in MPI and in a view that leads us to represent ADAPT as a graph of tasks intended to be ordered on a cloud computing platform. As a first contribution, we propose a parallelization of the diffusion-convection equation coupled to a linear systemin 2D and 3D using MPI. A two-level parallelization is used in our a implementation to take advantage of thecurrent distributed multicore machines. A balanced distribution of the computational load is obtained by using the decomposition of the domain using METIS, as well as a relevant resolution of our very large linear system using the parallel solver MUMPS (Massive Parallel MUltifrontal Solver). Our second contribution illustrates how to imagine the ADAPT framework, as depicted in the first contribution, as a Service. We transform the framework (in fact, a part of the framework) as a DAG (Direct Acyclic Graph) in order to see it as a scientific workflow. Then we introduce new policies inside the RedisDG workflow engine, in order to schedule tasks of the DAG, in an opportunistic manner. We introduce into RedisDG the possibility to work with dynamic workers (they can leave or enter into the computing system as they want) and a multi-criteria approach to decide on the “best” worker to choose to execute a task. Experiments are conducted on the ADAPT workflow to exemplify howfine is the scheduling and the scheduling decisions into the new RedisDG.

Master of Science / Department of Computing and Information Sciences / Daniel A. Andresen / A music player mobile application for Android is developed along with cloud server using Google’s App Engine and Firebase. This music player application provides various ways of navigating to an audio file and different music visualizer options. What’s more, the application also provides three major features: 1 user sign in and sign out, 2 display the most popular songs based on input, 3 users can submit comments and suggestions. These features are implemented by utilizing cloud services of Google’s App Engine and Firebase. Specifically, an application running on App Engine plays as a server’s role to verify user sign in. It also runs App Engine MapReduce jobs to consume large data stored in Google Cloud Storage and serves relatively small result about popular songs for the app. In addition, user’s comments and suggestions are automatically synchronized with Firebase which makes modifying and analyzing synchronized data really convenient.

abstract: Resource allocation is one of the most challenging issues policy decision makers must address. The objective of this thesis is to explore the resource allocation from an economical perspective, i.e., how to purchase resources in order to satisfy customers' requests. In this thesis, we attend to answer the question: when and how to buy resources to fulfill customers' demands with minimum costs? The first topic studied in this thesis is resource allocation in cloud networks. Cloud computing heralded an era where resources (such as computation and storage) can be scaled up and down elastically and on demand. This flexibility is attractive for its cost effectiveness: the cloud resource price depends on the actual utilization over time. This thesis studies two critical problems in cloud networks, focusing on the economical aspects of the resource allocation in the cloud/virtual networks, and proposes six algorithms to address the resource allocation problems for different discount models. The first problem attends a scenario where the virtual network provider offers different contracts to the service provider. Four algorithms for resource contract migration are proposed under two pricing models: Pay-as-You-Come and Pay-as-You-Go. The second problem explores a scenario where a cloud provider offers k contracts each with a duration and a rate respectively and a customer buys these contracts in order to satisfy its resource demand. This work shows that this problem can be seen as a 2-dimensional generalization of the classic online parking permit problem, and present a k-competitive online algorithm and an optimal online algorithm. The second topic studied in this thesis is to explore how resource allocation and purchasing strategies work in our daily life. For example, is it worth buying a Yoga pass which costs USD 100 for ten entries, although it will expire at the end of this year? Decisions like these are part of our daily life, yet, not much is known today about good online strategies to buy discount vouchers with expiration dates. This work hence introduces a Discount Voucher Purchase Problem (DVPP). It aims to optimize the strategies for buying discount vouchers, i.e., coupons, vouchers, groupons which are valid only during a certain time period. The DVPP comes in three flavors: (1) Once Expire Lose Everything (OELE): Vouchers lose their entire value after expiration. (2) Once Expire Lose Discount (OELD): Vouchers lose their discount value after expiration. (3) Limited Purchasing Window (LPW): Vouchers have the property of OELE and can only be bought during a certain time window. This work explores online algorithms with a provable competitive ratio against a clairvoyant offline algorithm, even in the worst case. In particular, this work makes the following contributions: we present a 4-competitive algorithm for OELE, an 8-competitive algorithm for OELD, and a lower bound for LPW. We also present an optimal offline algorithm for OELE and LPW, and show it is a 2-approximation solution for OELD. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2015