Cloud computing services made available to consumers range from providing basic computational resources such as storage and compute power to sophisticated enterprise application services. A common business model is to charge consumers on a pay-per-use basis where they periodically pay for the resources they have consumed. The provider is responsible for measuring and collecting the resource usage data. This approach is termed provider-side accounting. A serious limitation of this approach is that consumers have no choice but to take whatever usage data that is made available by the provider as trustworthy. This thesis investigates whether it is possible to perform consumer-side resource accounting where a consumer independently collects, for a given cloud service, all the data required for calculating billing charges. If this were possible, then consumers will be able to perform reasonableness checks on the resource usage data available from service providers as well as raise alarms when apparent discrepancies are suspected in consumption figures. Two fundamental resources of cloud computing, namely, storage and computing are evaluated. The evaluation exercise reveals that the resource accounting models of popular cloud service providers, such as Amazon, are not entirely suited to consumer-side resource accounting, in that discrepancies between the data collected by the provider and the consumer can occur. The thesis precisely identifies the causes that could lead to such discrepancies and points out how the discrepancies can be resolved. The results from the thesis can be used by service providers to improve their resource accounting models. In particular, the thesis shows how an accounting model can be made strongly consumer–centric so that all the data that the model requires for calculating billing charges can be collected independently by the consumer. Strongly consumer–centric accounting models have the desirable property of openness and transparency, since service users are in a position to verify the charges billed to them.

In the past five years, the phrase "cloud computing" has come into common usage to mean remotely-hosted computing services, where the provider of the service relies on one or more highly automated large-scale data centres as the computing infrastructure. With increasing demand for cloud computing, economies of scale are causing a race towards ever-larger data centres. With these increases in size comes a problematic growth in system complexity: Many conventional management techniques that work well when controlling a relatively small number of data-centre nodes become impracticable on larger scales. There is currently very little engineering theory, or experience-based teaching, that can be brought to bear on the design and management of large-scale data centres. One major issue facing traditional academic and industrial research facilities is that, contrary to the way data centre designers used to work, in the case of cloud data centres the systems they are able to use for detailed development and testing are always going to be much smaller than the final systems that go into production. For this reason, it is fair to characterise much of the current development work as being more art than science, and this imprecision can lead to costly errors. In almost all of current engineering practices, predictive simulation studies are used for rapid exploration and evaluation of design alternatives before they go into production. This helps avoiding costly mistakes. Despite this well-established tradition of computational modelling and simulation, there are currently no comparable tools for cloud-scale computing data centres. The research work described in this thesis is motivated by exactly that problem. I argue that there is a need for tools that allow owners and man- agers of cloud computing infrastructure to evaluate alternative designs, and to answer "what-if" questions. In many other areas of engineering, predictive computer simulation systems allow engineers to explore aspects of a design for some artefacts without that artefact actually having to be constructed. I have developed SPECI (Simulation Program for Elastic Cloud Infrastructures) and released it to the open-source community as a first step towards meeting this need. The research presented in this thesis covers several disciplines, presenting the cloud middleware model of component policy subscription updates that are used to manage services in the system, introducing simulation to this model, and using recent advances from complex network theory to model subscription distributions. It is a first step towards developing adaptive data-centre management policies that "intelligently" and dynamically organise and reorganise the network of components that work together within the data-centre in light of changing demands.

Cloud Computing will surpass the Internet in importance; it is a relatively new technology model of Internet-based computing, whereby information, software, storage, servers and networking are provided on demand. According to different end user requirements, such as data or computing intensive applications are applied in cloud environment. Cloud computing makes computing as a utility and 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. We review this cloud computing technologies, and indicate the main challenges for their development in future, among which resource management problem is pointed out and attracts our attention. Combing the current resource management and broker theories, we present new strategy of how to manage resources to deal with different user requirements of cloud services. We described the related work of resource management and broker architecture in this thesis, accomplishing four major research issues. Firstly, we present cloud resource management framework and service-oriented broker to efficiently utilize potential physical resource such as network, storage and computing resources. This framework provides on-demand service to users and optimizes resource utilization by employing virtualization technology. It has the ability to enhance the system performance and satisfy the many of user request. The service-oriented broker is presented in this framework. It is aim to discover, select and reserve the network and end-system resources. It is able to provide guaranteed service by reservation mechanism to meet user's demand. The several scheduling algorithms are employed for finding best match of combined resources. Secondly, we address the problem caused by failure of job submission in cloud environment. We present dynamic resource selection algorithm under user's QoS requirement which would benefit for selecting the best combined resource for real-time multimedia applications. This algorithm is aim to reduce the job fail rate and efficiently utilize network resource to enhance system performance. Thirdly, we solve the virtual network resource allocation problem in cloud computing. We present virtual network- aware resource scheduling algorithm for real-time multimedia applications that is in order to efficiently utilize physical network resources. This algorithm will reduce the physical network traffic and guarantee the service request of the users. Furthermore, the streaming data will be transmitted from selected data source, and then application performance will be maintained. Fourth, we address meta-job scheduling problem for computation application in cloud environment. We present V -heuristic scheduling algorithms for allocation of virtualized network and computing resource. The objective of these scheduling algorithms is aim to achieve high system throughput, improve load balance and minimize meta-job processing time. From the practical aspect, we develop a simulator which is extended from CloudSim simulator. This simulator offers a simulated environment to model this resource management framework. The CloudSim only concentrate on end-system resource without getting concerned about virtualization, technology and network resource, so that they can accelerate study progress of new cloud technologies.

Cloud computing provides a highly scalable, distributed infrastructure on which applications and data can be hosted. Hosted applications must perform within specified constraints, despite running over an extremely dynamic infrastructure. This dynamic behaviour is created, on one hand, by the applications running within virtual machines hosted on heterogeneous servers, and on the other by the constantly fluctuating demand on the network link between the virtual machine and the user of the application. This elasticity of infrastructure, and fluctuating demand placed on it, makes it difficult to ensure QoS for the application. It is no longer sufficient to measure the performance of various host servers within a data centre - it is also now required to measure the performance of the application where it is most critical with the user of the application. The efficiency of application hosting is also difficult to ensure. Optimisation of resource allocation should minimise the hosting cost while ensuring the delivery of the application to the end-user is acceptable. This thesis details a programme of research aimed at designing, implementing and using a distributed, highly-scalable system for monitoring and ensuring end-to-end, SLA-compliant performance of virtual private cloud applications, while minimising the overall hosting cost for the cloud. The research undertaken and described within this document reviews cloud computing, the underlying virtualisation technologies and optimisation techniques, and presents three models with completed prototypes. The first model is a cloud resource monitoring methodology, which acquires real- time metrics from live applications and hosts running in a virtual private cloud test- bed environment. This model is an adaptation of the Analytical Hierarchy Process (AHP), aimed at providing a SLA-compliant scoring mechanism for all criteria to be considered when measuring application performance. The model presented finally allows an overall score to be calculated by which the application's performance is assessed at any potential host where it may be placed. The second model is a cloud-centric network monitoring methodology, which analyses the quality of the network link between any two I.P. addresses. This system provides network quality information to be used in the assessment of the application's (potential) performance between any given host location and the end-user. The third model is an optimiser for SLA-compliant virtual machine placement within virtual private clouds, where the hosting cost is minimised. In summary, the key outputs of this thesis are: 1) a prototype vendor-agnostic monitoring system, for virtual private clouds, which provides the ability to acquire any metric, relevant to the successful delivery of an application to the end-user, and provide information regarding real-time SLA-compliance based on performance thresholds set for those metrics 2) a live system for projecting SLA-compliant application performance on any given host 3) a hierarchical framework for dynamically defining application SLAs, with any conceivable, acquirable metric 4) an optimisation engine with the goal of optimally placing applications on host servers with the goal of reducing the hosting cost across all host servers. The hosting costs for each application on each server is provided by the monitoring system 5) a software-based, virtualised tool for automatic monitoring of network performance, with a remote data logging component 6) An agent-based system for automatically and periodically acquiring VM and host metrics from a VMWare virtual private cloud. The thesis presents an overall fully-functional system incorporating all these aspects.

Distributed Systems as an area of research has seen a gradual evolution over the last few decades fuelled by the application of new use cases to technological developments. Cloud Computing is one such paradigm that has evolved from the adoption of Utility Computing, Virtualization and Service Oriented Architectures. Cloud Computing can be distinguished from other distributed paradigms though the provisioning of resources, data and software to users on demand in a similar fashion to the services provided by the electric power industry. Commercial Cloud offerings are expected to meet the Quality of Service (QoS) requirements of a consumer via Service Level Agreements (SLA). In reality, Cloud providers rarely provide QoS beyond best effort as the intrinsic fault tolerant nature of currently deployed applications require little more. Nevertheless, with enhancements to QoS in Cloud Computing the range of deployable applications can be improved and thus advance the overall adoption of the paradigm. This thesis tackles the shortcoming of QoS in Cloud Computing though novel enhancements to Cloud resource management. Since QoS is a broad subject area, the scope of research within has been narrowed down to two specific areas of interest: performance and scalability. In this thesis, the performance and scalability of Cloud technology are ascertained through performance evaluations on Hypervisor (such as XEN and KVM) and Cloud Infrastructure Managers (such as OpenNebula and Nimbus). Recommendations are made on how to resolve performance bottlenecks and on the suitability of certain technology for specific Cloud applications. Contextualisation and Re-contextualization mechanisms are introduced for self-configuring virtual Cloud resources at operation time while managing resources and software dependencies at the infrastructure and platform layer of the Cloud software stack. In addition, the thesis aims to improve the adoption of the Cloud by exploring novel techniques for composing, configuring and deploying Grid Middleware onto Cloud resources. The core contributions of this thesis are as follows: i) A prototype software tool for the (re-)contextualization of Cloud applications, platforms, infrastructures and resource dependencies that enables improvements to performance, scalability and fault tolerance. ii) Performance results and recommendations on the topic of Virtual Machine (VM) image propagation delay in Cloud infrastructure technology, Paravirtualized block device drivers and VM image standards in Hypervisor technology, for the purpose of ascertaining current limitations in Cloud QoS. iii) A software prototype system of an interoperable self-configuring Virtual Grid infrastructure, deployable on to a range of Cloud providers, to enhance the QoS achievable by Grid applications.

Today, mobile technology is part of everyday life and activities and the mobile ecosystems are blossoming, with smartphones and tablets being the major growth drivers. The mobile phones are no longer just another device, we rely on their capabilities in work and in private. We look to our mobile phones for timely and updated information and we rely on this being provided any time of any day at any place. Nevertheless, no matter how much you trust and love your mobile phone the quality of the information and the user experience is directly associated with the sources and presentation of information. In this perspective, our activities, interactions and preferences help shape the quality of service, content and products we use. Context-aware systems use such information about end-users as input mechanisms for producing applications based on mobile, location, social, cloud and customized content services. This represents new possibilities for extracting aggregated user-centric information and includes novel sources for context-aware applications. Accordingly, a Design Research based approach has been taken to further investigate the creation, presentation and tailoring of user-centric information. Through user evaluated experiments findings show how multi-dimensional context-aware information can be used to create adaptive solutions tailoring the user experience to the users’ needs. Research findings in this work; highlight possible architectures for integration of cloud computing services in a heterogeneous mobile environment in future context-aware solutions. When it comes to combining context-aware results from local computations with those of cloud based services, the results provide findings that give users tailored and adapted experiences based on the collective efforts of the two.

No keywords provided in dissertation / Cloud Computing has gained traction since its emergence and client organisations that want to benefit from the Cloud are looking for ways to migrate their on-premise applications to the Cloud. To assist client organisations with migration projects, researchers and practitioners have proposed various Cloud migration approaches. However, these approaches differ in applicability depending on the type of application being migrated and the Cloud Service Provider where the application is being migrated to. The various approaches to Cloud migration create complexity in Cloud migration decisions as client organisations have to consider various approaches depending on the migration project. The purpose of this dissertation is to create a universal Cloud migration approach that can be applied to every Cloud migration project. In this dissertation, a cloud migration decision framework is proposed; namely, A Holistic Cloud Migration Decision Framework (HCMDF). The research strategy that was followed is Design Science Research (DSR) and was selected since the output of the research is going to be an Information Technology (IT) research artefact. By applying the DSR strategy, the HCMDF was successfully developed and evaluated in the real world using an adaptive case study. The analysis of the results indicated that the HCMDF solves Cloud migration problem and that it can be applied to every Cloud migration project. Throughout the evaluation, areas of improvement were identified and these will be considered in future research. / School of Computing / M. Tech (Information Technology)