Cloud Computing represents a recent paradigm shift that enables users to share and remotely access high-powered computing resources (both infrastructure and software/services) contained in off-site data centers thereby allowing a more efficient use of hardware and software infrastructures. This growing trend in cloud computing, combined with the demands for Big Data and Big Data analytics, is driving the rapid evolution of datacenter technologies towards more cost-effective, consumer-driven, more privacy conscious and technology agnostic solutions. This dissertation is dedicated to taking a systematic approach to develop system-level techniques and algorithms to tackle the challenges of large-scale data processing in the Cloud and scaling and delivering privacy-aware services with anytime-anywhere availability. We analyze the key challenges in effective provisioning of Cloud services in the context of MapReduce-based parallel data processing considering the concerns of cost-effectiveness, performance guarantees and user-privacy and we develop a suite of solution techniques, architectures and models to support cost-optimized and privacy-preserving service provisioning in the Cloud. At the cloud resource provisioning tier, we develop a utility-driven MapReduce Cloud resource planning and management system called Cura for cost-optimally allocating resources to jobs. While existing services require users to select a number of complex cluster and job parameters and use those potentially sub-optimal per-job configurations, the Cura resource management achieves global resource optimization in the cloud by minimizing cost and maximizing resource utilization. We also address the challenges of resource management and job scheduling for large-scale parallel data processing in the Cloud in the presence of networking and storage bottlenecks commonly experienced in Cloud data centers. We develop Purlieus, a self-configurable locality-based data and virtual machine management framework that enables MapReduce jobs to access their data either locally or from close-by nodes including all input, output and intermediate data achieving significant improvements in job response time. We then extend our cloud resource management framework to support privacy-preserving data access and efficient privacy-conscious query processing. Concretely, we propose and implement VNCache: an efficient solution for MapReduce analysis of cloud-archived log data for privacy-conscious enterprises. Through a seamless data streaming and prefetching model in VNCache, Hadoop jobs begin execution as soon as they are launched without requiring any apriori downloading. At the cloud consumer tier, we develop mix-zone based techniques for delivering anonymous cloud services to mobile users on the move through Mobimix, a novel road-network mix-zone based framework that enables real time, location based service delivery without disclosing content or location privacy of the consumers.

Problem: Practical industry advancements associated with cloud computing in the contextof supply chain rapidly proliferate. However, there is a lack of academic research that investigatesthe application of this technology from a theoretical standpoint. A significantpart of the literature explores the perceived advantages and disadvantages related to the decision-making process of the adoption of the specific technology, rather than the perceivedbenefits and challenges when the cloud has been already adopted and used. Furthermore,absent from the literature is a thorough understanding of the cloud-based applications inthe industry of 3PLs, and the perceived benefits and challenges not only from the userχsside but from the supply side as well. The research is limited referring to critical aspects ofcloud computing applied on 3PLs within a theoretical basis. Thus, in this study, the authorsaim at filling these gaps by exploring what types of cloud-based services are applied on3PLs, what are the perceived benefits and challenges from the 3PLs perspective as well asfrom their cloud providers/IT companies. Purpose: The purpose of this thesis is to investigate what cloud-based services are used inthe 3PL industry as well as the challenges and benefits perceived by the cloud providers of3PL firms and by the 3PL firms that use this technology. In order to gain a thorough understandingof this usage and grasp the full picture within the focal industry, the research isfocusing on the perspectives of three cloud providers and three 3PLs that use and/or providecloud based services. Method: : This is a qualitative study. The authors are making use of a case study strategywith six investigated companies. The majority of the data is gathered from semi-structuredinterviews, while documentary secondary data concerning basic companiesχ information,have been collected as well. The analysis of the findings is based on the revision of the settedapriori codes by the authors. A cross-analysis between 3PLs and cloud providers ofthose firms is being conducted in order to identify the perceived challenges and benefits ofcloud by both, within the 3PL industry. Conclusion: The findings of this thesis demonstrate Software as a Service (SaaS) and moreprecisely, transportation administration (TA) software, as the most prevailing cloud-basedsystem applied in the 3PL industry nowadays. It has been revealed that 3PLs benefit fromcloud technology in terms of cost savings, strategic flexibility, access to leading-edge IT resourcesand security, whereas the challenges faced are related to performance and strategicaspect. Furthermore, the authors identify and propose the different phases of cloud computingimplementation in the 3PL sector. Lastly, a model of perceived challenges and benefitsof cloud in 3PLs firms has been compiled and presented.

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In this thesis we propose a cloud Input Shaper and Dynamic Resource Controller to provide application-level quality of service guarantees in cloud computing environments. The Input Shaper splits the cloud into two areas: one for shaped traffic that achieves quality of service targets, and one for overflow traffic that may not achieve the targets. The Dynamic Resource Controller profiles customers’ applications, then calculates and allocates the resources required by the applications to achieve given quality of service targets. The Input Shaper then shapes the rate of incoming requests to ensure that the applications achieve their quality of service targets based on the amount of allocated resources. To evaluate our system we create a new benchmark application that is suitable for use in cloud computing environments. It is designed to reflect the current design of cloud based applications and can dynamically scale each application tier to handle large and varying workload levels. In addition, the client emulator that drives the benchmark also mimics realistic user behaviors such as browsing from multiple tabs, using JavaScript, and has variable thinking and typing speeds. We show that a cloud management system evaluated using previous benchmarks could violate its estimated quality of service achievement rate by over 20%. The Input Shaper and Dynamic Resource Controller system consist of an application performance modeler, a resource allocator, decision engine, and an Apache HTTP server module to reshape the rate of incoming web requests. By dynamically allocating resources to applications, we show that their response times can be improved by as much as 30%. Also, the amount of resources required to host applications can be decreased by 20% while achieving quality of service objectives. The Input Shaper can reduce VMs’ resource utilization variances by 88%, and reduce the number of servers by 45%.

Cloud computing is becoming a viable option for Chief Information Officers (CIO’s) and business stakeholders to consider in today’s information technology (IT) environment, characterized by shrinking budgets and dynamic changes in the technology landscape. The objective of this study is to help Federal Government decision makers appropriately decide on the suitability of applications for migration to cloud computing. I draw from four theoretical perspectives: transaction cost theory, resource-based theory, agency theory and dynamic capabilities theory and use a conjoint analysis approach to understand stakeholder attitudes, opinions and behaviors in their decision to migrate applications to cloud computing. Based on a survey of 81 government cloud computing stakeholders, this research examined the relative importance of thirteen factors that organizations consider when migrating applications to cloud computing. Our results suggest that trust in the cloud computing vendor is the most significant factor, followed by the relative cost advantage, sensing capabilities and application complexity. A total of twelve follow-up interviews were conducted to provide explanation of our results. The contributions of the dissertation are twofold: 1) it provides novel insights into the relative importance of factors that influence government organizations’ decision to migrate applications to cloud computing, and 2) it assists senior government decision makers to appropriately weigh and prioritize the factors that are critical in application migration to cloud computing.

Cloud computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way in which hardware is designed and purchased. In this thesis, we reviewed the new cloud computing technologies, and indicated the main challenges for their development in future, among which resource management problem stands out and attracts our attention. Combining the current scheduling theories, we proposed cloud scheduling hierarchy to deal with different requirements of cloud services. From the theoretical aspects, we have accomplished three main research issues. Firstly, we solved the resource allocation problem in the user-level of cloud scheduling. We proposed game theoretical algorithms for user bidding and auctioneer pricing. With Bayesian learning prediction, resource allocation can reach Nash equilibrium among non-cooperative users even though common knowledge is insufficient. Secondly, we addressed the task scheduling problem in the system-level of cloud scheduling. We proved a new utilization bound for on-line schedulability test, considering the sequential feature of MapReduce. We deduced the relationship between cluster utilization bound and the ratio of Map to Reduce. This new schedulable bound with segmentation uplifts classic bound which is most used in industry. Thirdly, we settled the comparison problem among on-line schedulability tests in cloud computing. We proposed a concept of test reliability to evaluate the probability that a random task set could pass a given schedulability test. The larger the probability is, the more reliable the test is. From the aspect of system, a test with high reliability can guarantee high system utilization. From the practical aspects, we have developed a simulator to model MapReduce framework. This simulator offers a simulated environment directly used by MapReduce theoretical researchers. The users of SimMapReduce only concentrate on specific research issues without getting concerned about finer implementation details for diverse service models, so that they can accelerate study progress of new cloud technologies.

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.

Knowing what to sell, when to sell, and to whom to sell is essential buyer behavior insight to allocate scarce marketing resources efficiently and effectively. Applying the theory of relationship marketing (Morgan and Hunt 1994), this study seeks to investigate the link between commitment and trust and firm adoption of radically innovative information technology (IT). The construct of radical innovation is operationalized through the use of cloud computing. A review of the vast scholarly literature on radical innovation diffusion and adoption, and modeling techniques used to analyze buyer behavior is followed by empirical estimation of each of the radical innovation adoption questions of purchase timing, brand choice, and purchase amount. Then, the inefficiencies in the independent model process are highlighted, suggesting the need for an integrated model. Next, an integrated model is developed to link the purchase timing, brand choice, and purchase amount decisions. The essay concludes with insight for marketing practitioners on the strength of the factors of commitment and trust on adoption of radical innovation, an improved methodology for the business-to-business marketing literature, and potential further research paths.

The need for aggregating privacy policies is present in a variety of application areas today. In traditional client/server models, websites host services along with their policies in different private domains. However, in a cloud-computing platform where aggregators can merge multiple services, users often face complex decisions in terms of choosing the right services from service providers. In this computing paradigm, the ability to aggregate policies as well as services will be useful and more effective for users that are privacy conscious regarding their sensitive or personal information. This thesis studies the problems associated with the Platform for Privacy Preference (P3P) language, and the present issues with communicating and understanding the P3P language. Furthermore, it discusses some efficient strategies and algorithms for the matching and the merging processes, and then elaborates on some privacy policy conflicts that may occur after merging policies. Lastly, the thesis presents a tool for matching and merging P3P policies. If successful, the merge produces an aggregate policy that is consistent with the policies of all participating service providers.

Securing information systems from cyber attacks, malware and internal cyber threats is a difficult problem. Attacks on authentication and authorization (access control) is one of the more predominant and potentially rewarding attacks on distributed architectures. Attribute-Based Access Control (ABAC) is one of the more recent mechanisms to provide access control capabilities. ABAC combines the strength of cryptography with semantic expressions and relational assertions. By this composition, a powerful grammar is devised that can not only define complex and scalable access control policies, but defend against attacks on the policy itself. This thesis demonstrates how ABAC can be used as a primary access control solution for enterprise and commercial applications. / Graduate / 0984 / djbchepe@gmail.com