Cloud computing has revolutionized the computing landscape by providing on-demand, pay-as-you-go access to elastically scalable resources. Many applications are now being migrated from on-premises data centers to public clouds; yet, the transition to the cloud is not always straightforward and smooth. An application that performed well in an on-premise data center may not perform identically in public computing clouds, because many variables like virtualization can impact the application's performance. By collecting significant performance data through experimental study, the cloud's complexity particularly as it relates to performance can be revealed. However, conducting large-scale system experiments is particularly challenging because of the practical difficulties that arise during experimental deployment, configuration, execution and data processing. In spite of these associated complexities, we argue that a promising approach for addressing these challenges is to leverage automation to facilitate the exhaustive measurement of large-scale experiments. Automation provides numerous benefits: removes the error prone and cumbersome involvement of human testers, reduces the burden of configuring and running large-scale experiments for distributed applications, and accelerates the process of reliable applications testing. In our approach, we have automated three key activities associated with the experiment measurement process: create, manage and analyze. In create, we prepare the platform and deploy and configure applications. In manage, we initialize the application components (in a reproducible and verifiable order), execute workloads, collect resource monitoring and other performance data, and parse and upload the results to the data warehouse. In analyze, we process the collected data using various statistical and visualization techniques to understand and explain performance phenomena. In our approach, a user provides the experiment configuration file, so at the end, the user merely receives the results while the framework does everything else. We enable the automation through code generation. From an architectural viewpoint, our code generator adopts the compiler approach of multiple, serial transformative stages; the hallmarks of this approach are that stages typically operate on an XML document that is the intermediate representation, and XSLT performs the code generation. Our automated approach to large-scale experiments has enabled cloud experiments to scale well beyond the limits of manual experimentation, and it has enabled us to identify non-trivial performance phenomena that would not have been possible otherwise.

The evolution of cloud technologies which allows the provisioning of IT resources over the Internet promises many benefits for the individuals and enterprises alike. However, this new resource provisioning model comes with the security challenges which did not exist in the traditional resource procurement mechanisms. We focus on the possible security concerns of a cloud user (e.g. an organization, government department, etc.) to lease cloud services such as resources in the form of Virtual Machines (VM) from a public Infrastructure-as-a-Service (IaaS) provider. There are many security critical areas in the cloud systems, such as data confidentiality, resource integrity, service compliance, security audits etc. In this thesis, we focus on the security aspects which result in the trust deficit among the cloud stakeholders and hence hinder a security sensitive user to benefit from the opportunities offered by the cloud computing. Based upon our findings from the security requirements analysis,we propose solutions that enable user trust in the public IaaS clouds. Our solutions mainly deal with the secure life cycle management of the user VM which include mechanisms for VM launch and migration. The VM launch and migration solutions ensure that the user VM is always protected in the cloud by only allowing it to run on the user trusted platforms. This is done by using trusted computing techniques that allow the users to remotely attest and hence rate the cloud platforms trusted or untrusted. We also provide a prototype implementation to prove the implementation feasibility of the proposed trust enabling principles used in the VM launch and migration solutions.

Architecture, Engineering, and Construction (AEC) firms are in need to modify their business models to exploit available opportunities by the increasing role of knowledge management and information technology in the global economy. This thesis will propose business models for AEC firms to sell consulting knowledge on-line. The three proposed business models are: business and management intelligence, design optimization and innovation and; software customization and real time data analytics. The business models are based on selling online consulting knowledge services customized to the customers' business needs through enabling emerging information technologies like cloud computing. The models were developed through benchmarking cases in other industries, previous classifications in the area of business models and, experts insights. The suitability of these models to different clients will be assessed in addition to the most suitable revenue streams. Finally, the validated final business models will be presented in the framework by Osterwalder and Pigneur (2010).

Architecture, Engineering, and Construction (AEC) firms are in need to modify their business models to exploit available opportunities by the increasing role of knowledge management and information technology in the global economy. This thesis will propose business models for AEC firms to sell consulting knowledge on-line. The three proposed business models are: business and management intelligence, design optimization and innovation and; software customization and real time data analytics. The business models are based on selling online consulting knowledge services customized to the customers' business needs through enabling emerging information technologies like cloud computing. The models were developed through benchmarking cases in other industries, previous classifications in the area of business models and, experts insights. The suitability of these models to different clients will be assessed in addition to the most suitable revenue streams. Finally, the validated final business models will be presented in the framework by Osterwalder and Pigneur (2010).

In this thesis, we focused on two problems in mobile application development: contextualization and large-scale testing. We identified the limitations of current contextualization and testing solutions. On one hand, advanced-remote-computing- based mobilization does not provide context awareness to the mobile applications it mobilized, so we presented contextify to provide context awareness to them without rewriting the applications or changing their source code. Evaluation results and user surveys showed that contextify-contextualized applications reduce users' time and effort to complete tasks. On the other hand, current mobile application testing solutions cannot conduct tests at the UI level and in a large-scale manner simultaneously, so we presented and implemented automated cloud computing (ACT) to achieve this goal. Evaluation results showed that ACT can support a large number of users and it is stable, cost-efficiency as well as time-efficiency.

In this dissertation we take a peer-to-peer approach to deal with two specific issues, fair trading and file distribution, arisen from data management for cloud computing. In mobile cloud computing environment cloud providers may collaborate with each other and essentially organize some dedicated resources as a peer to peer sharing system. One well-known problem in such peer to peer systems with exchange of resources is free riding. Providing incentives for peers to contribute to the system is an important issue in peer to peer systems. We design a reputation-based fair trading mechanism that favors peers with higher reputation. Based on the definition of the reputation used in the system, we derive a fair trading policy. We evaluate the performance of reputation-based trading mechanisms and highlight the scenarios in which they can make a difference. Distribution of data to the resources within a cloud or to different collaborating clouds efficiently is another issue in cloud computing. The delivery efficiency is dependent on the characteristics of the network links available among these network nodes and the mechanism that takes advantage of them. Our study is based on the Global Environment for Network Innovations (GENI), a testbed for researchers to build a virtual laboratory at scale to explore future Internets. Our study consists of two parts. First, we characterize the links in the GENI network. Even though GENI has been used in many research and education projects, there is no systematic study about what we can expect from the GENI testbeds from a performance perspective. The goal is to characterize the links of the GENI networks and provide guidance for GENI experiments. Second, we propose a peer to peer approach to file distribution for cloud computing. We develop a mechanism that uses multiple delivery trees as the distribution structure, which takes into consideration the measured performance information in the GENI network. Files are divided into chunks to improve parallelism among different delivery trees. With a strict scheduling mechanism for each chunk, we can reduce the overall time for getting the file to all relevant nodes. We evaluate the proposed mechanism and show that our mechanism can significantly reduce the overall delivery time.

Data stored in third party storage systems like the cloud might not be secure since confidentiality and integrity of data are not guaranteed. Though cloud computing provides cost-effective storage services, it is a third party service and so, a client cannot trust the cloud service provider to store its data securely within the cloud. Hence, many organizations and users may not be willing to use the cloud services to store their data in the cloud until certain security guarantees are made. In this thesis, a solution to the problem of securely storing the client’s data by maintaining the confidentiality and integrity of the data within the cloud is developed. Five protocols are developed which ensure that the client’s data is stored only on trusted storage servers, replicated only on trusted storage servers, and guarantee that the data owners and other privileged users of that data access the data securely. The system is based on trusted computing platform technology [11]. It uses a Trusted Platform Module, specified by the Trusted Computing Group [11]. An encrypted file system is used to encrypt the user’s data. The system provides data security against a system administrator in the cloud.

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.

No description available.