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)

Denna avhandling utforskar den komplexa interaktionen mellan virtualiseringstekniker och prestanda för molnapplikationer. Genom att utvärdera hypervisor- och containerbaserade metoder skapar vi en teoretisk grund för att förstå rollen för virtualisering i modern molnbaserad miljö. Genom en metodisk förstudie och implementeringen av virtuella instanser samlar vi omfattande data om CPU-användning, nätverkstrafik, lagring och genomströmning, vilket avslöjar viktiga insikter. Resursanvändningen påverkas framför allt av hur databelastning ser ut, där resursintensiva uppgifter påverkar både hypervisor och containerinstanser. Studien visar att valet mellan hypervisor och containerteknik kan bero vissa användningsfall, vilket kräver ytterligare optimering. Att isolera tekniker på separata EC2- instanser ger bra kontroll men kräver löpande justeringar för varierande prestandamätningar. Slutsatserna hjälper till att förstå virtualiserings inverkan på molnapplikationer och betonar de avgörande rollerna för lagringshantering, nätverksdynamik och databelastning. Den här avhandlingen tar inte bara upp viktiga frågor, utan den lägger också grunden för framtida forskning som förutser förbättrad effektivitet och prestanda i virtualiserade molnmiljö. / This thesis explores the complex interaction between virtualization technologies and cloud application performance. By evaluating hypervisor and container-based approaches, we establish a theoretical foundation for understanding virtualization's role in modern cloud computing. Through a methodical pre-study and the implementation of virtual instances, we gather extensive data on CPU utilisation, network traffic, storage, and throughput to reveal key insights. Workloads notably impact resource usage, with resource-intensive tasks affecting both hypervisor and container instances. The study suggests that the choice between hypervisor and container technologies may depend on certain use cases, prompting further optimization. Isolating technologies on separate EC2 instances provides control but demands ongoing refinement for varied performance metrics. Conclusions contribute to understanding virtualization's impact on cloud applications, emphasising the critical roles of workload, network dynamics, and storage management. This thesis not only addresses key questions but also sets the stage for future explorations, promising enhanced performance and efficiency in virtualized cloud environments.

A framework for the quantitative analysis of performance, security and database management within a network system (e.g. a cloud computing platform) is presented within this research. Our study provides a methodology for modelling and quantitatively generating significant metrics needed in the evaluation of a network system. To narrow this research, a study is carried-out into the quantitative modelling and analysis of performance, security and database management trade-offs in cloud computing platforms, based on Stochastic Activity Networks (SANs) and combined metrics. Cloud computing is an innovative distributed computing archetypal based on the infrastructure of the internet providing computational power, application, storage and infrastructure services. Security mechanisms such as: batch rekeying, intrusion detection, encryption/decryption or security protocols come at the expense of performance and computing resources consumption. Furthermore, database management processing also has an adverse effect on performance especially in the presence of big data. Stochastic Activity Networks (SANs) that offer synchronisation, timeliness and parallelism are proposed for the modelling and quantitative evaluations of ‘optimal’ trade-offs involving performance, security and database management. Performance modelling and analysis of computer network systems has mostly been considered of utmost importance. Quantification of performance for a while has been assessed using stochastic models with a rising interest in the quantification of security stochastic modelling being applied to security problems. Quantitative techniques that includes analytical valuations founded on queuing theory, discrete-event simulations and correlated approximations have been utilised in the examination of performance. Security suffers from the point that no interpretations can be made in an optimal case. The most consequential security metrics are in analogy with reliability metrics. The express rate at which data grows increases the prominence for research into the design and development of cloud computing models that manages the workload intensity and are suitable for data exploration. Handling big data especially within cloud computing is a resource consuming, time-demanding and challenging task that necessitates titanic computational infrastructures to endorse successful data exploration. We present an improved Security State Transition Diagram (SSTD) by adding a new security state (Failed/Freeze state). The presence of this new security state signifies a security position of the computing network system were the implemented security countermeasures cannot handle the security attacks and the system fails completely. In a more sophisticated security system, when the security countermeasure(s) cannot in any form categorise the security attack, the network system is moved to the Failed/Freeze security state. At this security state, the network system can only resume operation when restored by the system administrator. In this study, we propose a cloud computing system model, defined security countermeasures and evaluated the optimisation problems for the trade-offs between performance, security and database management using SANs formalism. We designed, modelled and implemented dependency within our presented security system, developing interaction within the security countermeasures using our proposed Security Group Communication System (SGCS). The choice of Petri-Nets enables the understanding and capturing of specified metrics at different stages of the proposed cloud computing model. In this thesis, an overview of cloud computing including its classification and services is presented in conjunction with a review of existing works of literature. Subsequently, a methodology is proposed for the quantitative analysis of our proposed cloud computing model of performance-security-database trade-offs using Möbius simulator. Additionally, numerical experiments with relevant interpretations are presented and appropriate interpretations are made. We identified that there are system parameters that can be used to optimise the presented abstract combined metrics but they are optimal for neither performance or security or database management independently. Founded on the proposed quantitative simulation model framework, reliable numerical experiments were observed and indicated scope for further extensions of this work. For example, the use of Machine Learning (ML) or Artificial Intelligence (AI) in the predictive and prevention aspects of the security systems.

La couche Infrastructure- as-a-Service (IaaS) de Cloud Computing offre un service de déploiement des images de machines virtuelles (VMIs) à la demande. Ce service fournit une plate-forme flexible pour les utilisateurs de cloud computing pour développer , déployer et tester leurs applications. Le déploiement d'une VMI implique généralement le démarrage de l'image, l'installation et la configuration des paquets de logiciels. Dans l'approche traditionnelle, lorsqu'un utilisateur de cloud demande une nouvelle plate-forme, le fournisseur de cloud sélectionne une image de modèle approprié pour cloner et déployer sur ​​les nœuds de cloud​​. L'image de modèle contient des paquets de logiciel pré-installés. Si elle ne correspond pas aux exigences, alors elle sera personnalisée ou la nouvelle image sera créé à partir de zéro pour s'adapter à la demande. Dans le cadre de la gestion des services de cloud, l'approche traditionnelle face aux questions difficiles de la manipulation de la complexité de l'interdépendance entre les paquets de logiciel, mise à l'échelle et le maintien de l' image déployée à l'exécution. Les fournisseurs de cloud souhaitent automatiser ce processus pour améliorer la performance de processus d'approvisionnement des VMIs, et de donner aux utilisateurs de cloud plus de flexibilité pour la sélection ou la création des images appropriées, tout en maximisant les avantages pour les fournisseurs en termes de temps, de ressources et de coût opérationnel. Cette thèse propose une approche pour gérer l'interdépendance des paquets de logiciels, pour modéliser et automatiser le processus de déploiement VMIs, et pour soutenir la reconfiguration VMIS à l'exécution, appelée l'approche dirigée par les modèle (Model-Driven approach). Nous nous adressons particulièrement aux défis suivants: (1) la modélisation de la variabilité des configurations d'image de machine virtuelle, (2) la réduction la quantité de transfert de données à travers le réseau, (3) l'optimisation de la consommation d'énergie des machines virtuelles; (4) la facilité à utiliser pour les utilisateurs de cloud; (5) l'automatisation du déploiement des VMIs; (6) le support de la mise à l'échelle et la reconfiguration de VMIS à l'exécution; (7) la manipulation de la topologie de déploiement complexe des VMIs . Dans notre approche, nous utilisons des techniques d'ingénierie dirigée par les modèles pour modéliser les représentations d'abstraction des configurations de VMI, le déploiement et les processus de reconfiguration d'image de machine virtuelle. Nous considérons que les VMIS comme une gamme de produits et utiliser les modèles de caractère pour représenter les configurations de VMIs. Nous définissons également le déploiement, les processus de reconfiguration et leurs facteurs (par exemple: les images de machines virtuelles, les paquets de logiciel, la plate-forme, la topologie de déploiement, etc.) comme les modèles. D'autre part, l'approche dirigée par les modèles s'appuie sur les abstractions de haut niveau de la configuration de VMIs et le déploiement de VMIs pour rendre la gestion d'images virtuelles dans le processus d'approvisionnement pour être plus flexible et plus facile que les approches traditionnelles. / The Cloud Computing Infastructure-as-a-Service (IaaS) layer provides a service for on demand virtual machine images (VMIs) deployment. This service provides a flexible platform for cloud users to develop, deploy, and test their applications. The deployment of a VMI typically involves booting the image, installing and configuring the software packages. In the traditional approach, when a cloud user requests a new platform, the cloud provider selects an appropriate template image for cloning and deploying on the cloud nodes. The template image contains pre-installed software packages. If it does not fit the requirements, then it will be customized or the new one will be created from scratch to fit the request. In the context of cloud service management, the traditional approach faces the difficult issues of handling the complexity of interdependency between software packages, scaling and maintaining the deployed image at runtime. The cloud providers would like to automate this process to improve the performance of the VMIs provisioning process, and to give the cloud users more flexibility for selecting or creating the appropriate images while maximizing the benefits for providers intern of time, resources and operational cost. This thesis proposes an approach to manage the interdependency of the software packages, to model and automate the VMIs deployment process, to support the VMIs reconfiguration at runtime, called the Model-Driven approach. We particularly address the following challenges: (1) modeling the variability of virtual machine image configurations; (2) reducing the amount of data transfer through the network; (3) optimizing the power consumption of virtual machines; (4) easy-to-use for cloud users; (5) automating the deployment of VMIs; (6) supporting the scaling and reconfiguration of VMIs at runtime; (7) handling the complex deployment topology of VMIs. In our approach, we use Model-Driven Engineering techniques to model the abstraction representations of the VMI configurations, the deployment and the reconfiguration processes of virtual machine image. We consider the VMIs as a product line and use the feature models to represent the VMIs configurations. We also define the deployment, re-configuration processes and their factors (e.g. virtual machine images, software packages, platform, deployment topology, etc.) as the models. On the other hand, the Model-Driven approach relies on the high-level abstractions of the VMIs configuration and the VMIs deployment to make the management of virtual images in the provisioning process to be more flexible and easier than traditional approaches.

Decisions to deploy IT systems on public Infrastructure-as-a-Service clouds can be complicated as evaluating the benefits, risks and costs of using such clouds is not straightforward. The aim of this project was to investigate the challenges that enterprises face when making system deployment decisions in public clouds, and to develop vendor-neutral tools to inform decision makers during this process. Three tools were developed to support decision makers: 1. Cloud Suitability Checklist: a simple list of questions to provide a rapid assessment of the suitability of public IaaS clouds for a specific IT system. 2. Benefits and Risks Assessment tool: a spreadsheet that includes the general benefits and risks of using public clouds; this provides a starting point for risk assessment and helps organisations start discussions about cloud adoption. 3. Elastic Cost Modelling: a tool that enables decision makers to model their system deployment options in public clouds and forecast their costs. These three tools collectively enable decision makers to investigate the benefits, risks and costs of using public clouds, and effectively support them in making system deployment decisions. Data was collected from five case studies and hundreds of users to evaluate the effectiveness of the tools. This data showed that the cost effectiveness of using public clouds is situation dependent rather than universally less expensive than traditional forms of IT provisioning. Running systems on the cloud using a traditional 'always on' approach can be less cost effective than on-premise servers, and the elastic nature of the cloud has to be considered if costs are to be reduced. Decision makers have to model the variations in resource usage and their systems' deployment options to obtain accurate cost estimates. Performing upfront cost modelling is beneficial as there can be significant cost differences between different cloud providers, and different deployment options within a single cloud. During such modelling exercises, the variations in a system's load (over time) must be taken into account to produce more accurate cost estimates, and the notion of elasticity patterns that is presented in this thesis provides one simple way to do this.

In dieser Dissertation werden verschiedene Anforderungen an sicheres Cloud Computing untersucht. Insbesondere geht es dabei um die Analyse bestehender Forschungs- und Lösungsansätze zum Schutz von Daten und Prozessen in Cloud-Umgebungen und um die Bewertung ihrer Praxistauglichkeit. Die Basis für die Vergleichbarkeit stellen spezifizierte Kriterien dar, nach denen die untersuchten Technologien bewertet werden. Hauptziel dieser Arbeit ist zu zeigen, auf welche Weise technische Forschungsansätze verglichen werden können, um auf dieser Grundlage eine Bewertung ihrer Eignung in der Praxis zu ermöglichen. Hierzu werden zunächst relevante Teilbereiche der Cloud Computing Sicherheit aufgezeigt, deren Lösungsstrategien im Kontext der Arbeit diskutiert und State-of-the-Art Methoden evaluiert. Die Aussage zur Praxistauglichkeit ergibt sich dabei aus dem Verhältnis des potenziellen Nutzens zu den damit verbundene erwartenden Kosten. Der potenzielle Nutzen ist dabei als Zusammenführung der gebotenen Leistungsfähigkeit, Sicherheit und Funktionalität der untersuchten Technologie definiert. Zur objektiven Bewertung setzten sich diese drei Größen aus spezifizierten Kriterien zusammen, deren Informationen direkt aus den untersuchten Forschungsarbeiten stammen. Die zu erwartenden Kosten ergeben sich aus Kostenschlüsseln für Technologie, Betrieb und Entwicklung. In dieser Arbeit sollen die zugleich spezifizierten Evaluierungskriterien sowie die Konstellation der obig eingeführten Begriffe ausführlich erläutert und bewertet werden. Für die bessere Abschätzung der Eignung in der Praxis wird in der Arbeit eine angepasste SWOT-Analyse für die identifizierten relevanten Teilbereiche durchgeführt. Neben der Definition der Praktikabilitätsaussage, stellt dies die zweite Innovation dieser Arbeit dar. Das konkrete Ziel dieser Analyse ist es, die Vergleichbarkeit zwischen den Teilbereichen zu erhöhen und so die Strategieplanung zur Entwicklung sicherer Cloud Computing Lösungen zu verbessern.

No description available.

Organizations are turning to private clouds due to concerns about security, privacy and administrative control. They are attracted by the flexibility and other advantages of cloud computing but are wary of breaking decades-old institutional practices and procedures. Private Clouds can help to alleviate these concerns by retaining security policies, in-organization ownership and providing increased accountability when compared with public services. This work investigates how it may be possible to develop an energy-aware private cloud system able to adapt workload allocation strategies so that overall energy consumption is reduced without loss of performance or dependability. Current literature focuses on consolidation as a method for improving the energy-efficiency of cloud systems, but if consolidation is undesirable due to the performance penalties, dependability or latency then another approach is required. Given a private cloud in which the machines are constant, with no machines being powered down in response to changing workloads, and a set of virtual machines to run, each with different characteristics and profiles, it is possible to mix the virtual machine placement to reduce energy consumption or improve performance of the VMs. Through a series of experiments this work demonstrates that workload mixes can have an effect on energy consumption and the performance of applications running inside virtual machines. These experiments took the form of measuring the performance and energy usage of applications running inside virtual machines. The arrangement of these virtual machines on their hosts was varied to determine the effect of different workload mixes. The insights from these experiments have been used to create a proof-of- concept custom VM Allocator system for the OpenStack private cloud computing platform. Using CloudMonitor, a lightweight monitoring application to gather data on system performance and energy consumption, the implementation uses a holistic view of the private cloud state to inform workload placement decisions.

Commercial and private cloud providers offer virtualized resources via a set of co-located and dedicated hosts that are exclusively reserved for the purpose of offering a cloud service. While both cloud models appeal to the mass market, there are many cases where outsourcing to a remote platform or procuring an in-house infrastructure may not be ideal or even possible. To offer an attractive alternative, we introduce and develop an ad hoc cloud computing platform to transform spare resource capacity from an infrastructure owner’s locally available, but non-exclusive and unreliable infrastructure, into an overlay cloud platform. The foundation of the ad hoc cloud relies on transferring and instantiating lightweight virtual machines on-demand upon near-optimal hosts while virtual machine checkpoints are distributed in a P2P fashion to other members of the ad hoc cloud. Virtual machines found to be non-operational are restored elsewhere ensuring the continuity of cloud jobs. In this thesis we investigate the feasibility, reliability and performance of ad hoc cloud computing infrastructures. We firstly show that the combination of both volunteer computing and virtualization is the backbone of the ad hoc cloud. We outline the process of virtualizing the volunteer system BOINC to create V-BOINC. V-BOINC distributes virtual machines to volunteer hosts allowing volunteer applications to be executed in the sandbox environment to solve many of the downfalls of BOINC; this however also provides the basis for an ad hoc cloud computing platform to be developed. We detail the challenges of transforming V-BOINC into an ad hoc cloud and outline the transformational process and integrated extensions. These include a BOINC job submission system, cloud job and virtual machine restoration schedulers and a periodic P2P checkpoint distribution component. Furthermore, as current monitoring tools are unable to cope with the dynamic nature of ad hoc clouds, a dynamic infrastructure monitoring and management tool called the Cloudlet Control Monitoring System is developed and presented. We evaluate each of our individual contributions as well as the reliability, performance and overheads associated with an ad hoc cloud deployed on a realistically simulated unreliable infrastructure. We conclude that the ad hoc cloud is not only a feasible concept but also a viable computational alternative that offers high levels of reliability and can at least offer reasonable performance, which at times may exceed the performance of a commercial cloud infrastructure.

Context: Software testing is the process of assessing quality of a software product to determine whether it matches with the existing requirements of the customer or not. Software testing is one of the “Verification and Validation,” or V&V, software practices. The two basic techniques of software testing are Black-box testing and White box testing. Black-box testing focuses solely on the outputs generated in response to the inputs supplied neglecting the internal components of the software. Whereas, White-box testing focuses on the internal mechanism of the software of any application. To explore the feasibility of black-box and white-box testing under a given set of conditions, a proper test automation framework needs to be deployed. Automation is deployed in order to reduce the manual effort and to perform testing continuously, thereby increasing the quality of the product. Objectives: In this research, cloud hosted application is automated using TestComplete tool. The objective of this thesis is to verify the functionality of cloud application such as test appliance library through automation and to measure the impact of the automation on release cycles of the organisation. Methods: Here automation is implemented using scrum methodology which is an agile development software process. Using scrum methodology, the product with working software can be delivered to the customers incrementally and empirically with updating functionalities in it. Test appliance library functionality is verified deploying testing device thereby keeping track of automatic software downloads into the testing device and licenses updating in the testing device. Results: Automation of test appliance functionality of cloud hosted application is made using TestComplete tool and impact of automation on release cycles is found reduced. Through automation of cloud hosted application, nearly 24% of reduction in level of release cycles can be observed thereby reducing the manual effort and increasing the quality of delivery. Conclusion: Automation of a cloud hosted application provides no manual effort thereby utilisation of time can be made effectively and application can be tested continuously increasing the efficiency and / AUTOMATION OF A CLOUD HOSTED APPLICATION