Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 96-99).

Th. 3e cycle--Urban.--Paris 8, 1988.

Η παρούσα διπλωματική έχει σαν σκοπό τη διερεύνηση της τεχνολογίας του Cloud Computing και της τεχνολογίας της Virtualization που την στηρίζει. Παρουσίαση της ιστορίας και μια τεχνική παρουσίαση των δυνατοτήτων και των καταβολών των τεχνολογιών. Αναφέρονται πρακτικές εφαρμογές που μπορούν οι συγκεκριμένες τεχνολογίες να εφαρμοστούν και τους σκοπούς που θα εξυπηρετήσουν. Επίσης γίνεται μια πιο αναλυτική παρουσίαση δυο προγραμμάτων (Xen Hypervisor – για το επίπεδο της Virtualization, Eucalyptus – σαν πλατφόρμα για τη δημιουργία IaaS Clouds). Παρουσιάζονται επίσης σύντομοι οδηγοί για την εγκατάσταση ενός Cloud, καθώς και το configuration μαζί με τους λόγους που χρησιμοποιήθηκε. / The scope of this thesis is to study the technology of Cloud Computing and the Virtualization technology that is supporting it. A presentation of the history, a technical overview and the origins of these technologies. There are mentioned some fields that the specified technologies could apply and the purposes that they would serve. On the third chapter, a more detailed presentation of two pieces of software is given (Xen Hypervisor – for the Virtualization Layer, Eucalyptus – as the platform to create IaaS Clouds). In the end quick how-to guides are described on the procedure to install a Cloud, the configuration and the reasons of the specific set up as well.

Stemming from a 2011 incident inside of a Facebook data facility in which hyper-cooled air formed a literal (if somewhat transient) rain cloud in the stacks, It was raining in the data center examines ideas of non-places and supermodernity applied to contemporary network infrastructure. It was raining in the data center argues that the problem of the rain cloud is as much a problem of psychology as it is a problem of engineering. Although humidity-management is a predictable snag for any data center, the cloud was a surprise; a self-inflicted side-effect of a strategy of distance. The rain cloud was a result of the same rhetoric of ephemerality that makes it easy to imagine the inside of a data center to be both everywhere and nowhere. This conceit of internet data being placeless shares roots with Marc Augé’s idea of non-places (airports, highways, malls), which are predicated on the qualities of excess and movement. Without long-term inhabitants, these places fail to tether themselves to their locations, instead existing as a markers of everywhere. Such a premise allows the internet to exist as an other-space that is not conceptually beholden to the demands of energy and landscape. It also liberates the idea of ‘the network’ from a similar history of industry. However, the network is deeply rooted in place, as well as in industry and transit. Examining the prevalence of network overlap in American fiber-optic cabling, it becomes easy to trace routes of cables along major US freight train lines and the US interstate highway system. The historical origin of this network technology is in weaponization and defense, from highways as a nuclear-readiness response to ARPANET’s Pentagon-based funding. Such a linkage with the military continues today, with data centers likely to be situated near military installations— sharing similar needs electricity, network connectivity, fair climate, space, and invisibility. We see the repetition of militarized tropes across data structures. Fiber-optic network locations are kept secret; servers are housed in cold-war bunkers; data centers nest next to military black-sites. Similarly, Augé reminds us that non-places are a particular target of terrorism, populated as they are with cars, trains, drugs and planes that turn into weapons. When the network itself is at threat of weaponization, the effect is an ambient and ephemeral fear; a paranoia made of over-connection.

La présente thèse s'intéresse à la classification collaborative dans un contexte de données complexes, notamment dans le cadre du Big Data, nous nous sommes penchés sur certains paradigmes computationels pour proposer de nouvelles approches en exploitant des technologies de calcul intensif et large echelle. Dans ce cadre, nous avons mis en oeuvre des classifieurs massifs, au sens où le nombre de classifieurs qui composent le multi-classifieur peut être tres élevé. Dans ce cas, les méthodes classiques d'interaction entre classifieurs ne demeurent plus valables et nous devions proposer de nouvelles formes d'interactions, qui ne se contraignent pas de prendre la totalité des prédictions des classifieurs pour construire une prédiction globale. Selon cette optique, nous nous sommes trouvés confrontés à deux problèmes : le premier est le potientiel de nos approches à passer à l'echelle. Le second, relève de la diversité qui doit être créée et maintenue au sein du système, afin d'assurer sa performance. De ce fait, nous nous sommes intéressés à la distribution de classifieurs dans un environnement de Cloud-computing, ce système multi-classifieurs est peut etre massif et ses propréités sont celles d'un système complexe. En terme de diversité des données, nous avons proposé une approche d'enrichissement de données d'apprentissage par la génération de données de synthèse, à partir de modèles analytiques qui décrivent une partie du phenomène étudié. Aisni, la mixture des données, permet de renforcer l'apprentissage des classifieurs. Les expérientations menées ont montré un grand potentiel pour l'amélioration substantielle des résultats de classification. / This thesis focuses on the collaborative classification in the context of complex data, in particular the context of Big Data, we used some computational paradigms to propose new approaches based on HPC technologies. In this context, we aim at offering massive classifiers in the sense that the number of elementary classifiers that make up the multiple classifiers system can be very high. In this case, conventional methods of interaction between classifiers is no longer valid and we had to propose new forms of interaction, where it is not constrain to take all classifiers predictions to build an overall prediction. According to this, we found ourselves faced with two problems: the first is the potential of our approaches to scale up. The second, is the diversity that must be created and maintained within the system, to ensure its performance. Therefore, we studied the distribution of classifiers in a cloud-computing environment, this multiple classifiers system can be massive and their properties are those of a complex system. In terms of diversity of data, we proposed a training data enrichment approach for the generation of synthetic data from analytical models that describe a part of the phenomenon studied. so, the mixture of data reinforces learning classifiers. The experimentation made have shown the great potential for the substantial improvement of classification results.

Arctic clouds are poorly represented in numerical models due to the complex, small-scale interactions which occur within them. Modelled cloud fractions are often significantly less than observed in this region; therefore, the radiative budget is not accurately simulated and forecasts of the melting cryosphere are fraught with uncertainty. Our ability to accurately model Arctic clouds can be improved through observational studies. Recent in situ airborne measurements from the springtime Aerosol-Cloud Coupling and Climate Interactions in the Arctic (ACCACIA) campaign are presented in this thesis to improve our understanding of the cloud microphysical interactions unique to this region. Aerosol-cloud interactions - where aerosol particles act as ice nucleating particles (INPs) or cloud condensation nuclei (CCN) - are integral to the understanding of clouds on a global scale. In the Arctic, uncertainties caused by our poor understanding of these interactions are enhanced by strong feedbacks between clouds, the boundary layer, and the sea ice. In the Arctic spring, aerosol-cloud interactions are affected by the Arctic haze, where a stable boundary layer allows aerosol particles to remain in the atmosphere for long periods of time. This leads to a heightened state of mixing in the aerosol population, which affects the ability of particles to act as INPs or CCN. Aerosol particle compositional data are presented to indicate which particles are present during the ACCACIA campaign, and infer how they may participate in aerosol-cloud interactions. Mineral dusts (known INPs) are identified in all flights considered, and the dominating particle classes in each case vary with changing air mass history. Mixed particles, and an enhanced aerosol loading, are identified in the final case. Evidence is presented which suggests these characteristics may be attributed to biomass burning activities in Siberia and Scandinavia. Additionally, in situ airborne observations are presented to investigate the relationship between the Arctic atmosphere and the mixed-phase clouds - containing both liquid cloud droplets and ice crystals - common to this region. Cloud microphysical structure responds strongly to changing surface conditions, as strong heat and moisture fluxes from the comparatively-warm ocean promote more turbulent motion in the boundary layer than the minimal heat fluxes from the frozen sea ice. Observations over the transition from sea ice to ocean show that the cloud liquid water content increases four-fold, whilst ice crystal number concentrations, N_ice, remain consistent at ~0.5/L. Following from this study, large eddy simulations are used to illustrate the sensitivity of cloud structure, evolution, and lifetime to N_ice. To accurately model mixed-phase conditions over sea ice, marginal ice, and ocean, ice nucleation must occur under water-saturated conditions. Ocean-based clouds are found to be particularly sensitive to N_ice, as small decreases in N_ice allow glaciating clouds to be sustained, with mixed-phase conditions, for longer. Modelled N_ice also influences precipitation development over the ocean, with either snow or rain depleting the liquid phase of the simulated cloud.

Denial of Service (DoS) attacks remain a serious threat to internet stability. A specific kind of low bandwidth DoS attack, called a slow rate attack can with very limited resources potentially cause major interruptions to the availability of the attacked web servers. This thesis examines the impact of slow rate application layer DoS attacks against three different server solutions. The server solutions are a static cloud solution and a load-balancing cloud solution running on AmazonWeb Services (AWS) as well as a dedicated server. To identify the impact in terms of responsiveness and service availability a number of experiments were conducted on the web servers using publicly available DoS tools. The response times of the requests were measured. The results show that the dedicated and static cloud based server solutions are severely impacted by the attacks while the AWS load-balancing cloud solution is not impacted nearly as much. We concluded that all solutions were impacted by the attacks and that the readily available DoS tools are sufficient for creating a denial of service state on certain web servers.

abstract: This dissertation is focused on building scalable Attribute Based Security Systems (ABSS), including efficient and privacy-preserving attribute based encryption schemes and applications to group communications and cloud computing. First of all, a Constant Ciphertext Policy Attribute Based Encryption (CCP-ABE) is proposed. Existing Attribute Based Encryption (ABE) schemes usually incur large, linearly increasing ciphertext. The proposed CCP-ABE dramatically reduces the ciphertext to small, constant size. This is the first existing ABE scheme that achieves constant ciphertext size. Also, the proposed CCP-ABE scheme is fully collusion-resistant such that users can not combine their attributes to elevate their decryption capacity. Next step, efficient ABE schemes are applied to construct optimal group communication schemes and broadcast encryption schemes. An attribute based Optimal Group Key (OGK) management scheme that attains communication-storage optimality without collusion vulnerability is presented. Then, a novel broadcast encryption model: Attribute Based Broadcast Encryption (ABBE) is introduced, which exploits the many-to-many nature of attributes to dramatically reduce the storage complexity from linear to logarithm and enable expressive attribute based access policies. The privacy issues are also considered and addressed in ABSS. Firstly, a hidden policy based ABE schemes is proposed to protect receivers' privacy by hiding the access policy. Secondly,a new concept: Gradual Identity Exposure (GIE) is introduced to address the restrictions of hidden policy based ABE schemes. GIE's approach is to reveal the receivers' information gradually by allowing ciphertext recipients to decrypt the message using their possessed attributes one-by-one. If the receiver does not possess one attribute in this procedure, the rest of attributes are still hidden. Compared to hidden-policy based solutions, GIE provides significant performance improvement in terms of reducing both computation and communication overhead. Last but not least, ABSS are incorporated into the mobile cloud computing scenarios. In the proposed secure mobile cloud data management framework, the light weight mobile devices can securely outsource expensive ABE operations and data storage to untrusted cloud service providers. The reported scheme includes two components: (1) a Cloud-Assisted Attribute-Based Encryption/Decryption (CA-ABE) scheme and (2) An Attribute-Based Data Storage (ABDS) scheme that achieves information theoretical optimality. / Dissertation/Thesis / Ph.D. Computer Science 2011

abstract: In 1999, Geneviève Hasenohr announced the discovery of a fragment of Marguerite Porete's Mirouer des Simples Ames, a work condemned by the Church at the University of Paris in 1310, hidden in a manuscript at the Bibliothèque municipale in Valenciennes. The fragment corresponds with roughly two chapters in the only extant French version of the manuscript (Chantilly, Musée Condé MS F XIV 26), and when compared with other editions of the Mirouer, it appears to be composed in what might have been Marguerite Porete's native dialect. The discovery changed scholars' perceptions of the weight of the various versions and translations - the Chantilly manuscript had been used previously to settle any questions of discrepancy, but now it appears that the Continental Latin and Middle English translations should be the arbiters. This discovery has elevated the Middle English editions, and has made the question of the translator's identity - he is known only by his initials M.N. - and background more imperative to an understanding of why a work with such a dubious history would be translated and harbored by English Carthusians in the century that followed its condemnation. The only candidate suggested for translator of the Mirouer has been Michael Northburgh (d. 1361), the Bishop of London and co-founder of the London Charterhouse, where two of the three remaining copies of the translation were once owned, but the language of the text and Northburgh's own position and interests do not fit this suggestion. My argument is that the content of the book, the method of its translation, its selection as a work for a Latin-illiterate audience, all fit within the interests of a circle of writers based in Yorkshire at the end of the fourteenth century. By beginning among the Yorkshire circle, and widening the search to include writers with a non-traditional contemplative audience, one that exists outside of the cloister - writers like Walter Hilton, the anonymous authors of the Cloud of Unknowing and the Chastising of God's Children, and Nicholas Love - we may have a better chance of locating and understanding the motives of the Middle English translator of the Mirouer. / Dissertation/Thesis / Ph.D. English 2011

Given the evolution of Cloud Computing in recent years, users and clients adopting Cloud Computing for both personal and business needs have increased at an unprecedented scale. This has naturally led to the increased deployments and implementations of Cloud datacentres across the globe. As a consequence of this increasing adoption of Cloud Computing, Cloud datacentres are witnessed to be massive energy consumers and environmental polluters. Whilst the energy implications of Cloud datacentres are being addressed from various research perspectives, predicting the future trend and behaviours of workloads at the datacentres thereby reducing the active server resources is one particular dimension of green computing gaining the interests of researchers and Cloud providers. However, this includes various practical and analytical challenges imposed by the increased dynamism of Cloud systems. The behavioural characteristics of Cloud workloads and users are still not perfectly clear which restrains the reliability of the prediction accuracy of existing research works in this context. To this end, this thesis presents a comprehensive descriptive analytics of Cloud workload and user behaviours, uncovering the cause and energy related implications of Cloud Computing. Furthermore, the characteristics of Cloud workloads and users including latency levels, job heterogeneity, user dynamicity, straggling task behaviours, energy implications of stragglers, job execution and termination patterns and the inherent periodicity among Cloud workload and user behaviours have been empirically presented. Driven by descriptive analytics, a novel user behaviour forecasting framework has been developed, aimed at a tri-fold forecast of user behaviours including the session duration of users, anticipated number of submissions and the arrival trend of the incoming workloads. Furthermore, a novel resource optimisation framework has been proposed to avail the most optimum level of resources for executing jobs with reduced server energy expenditures and job terminations. This optimisation framework encompasses a resource estimation module to predict the anticipated resource consumption level for the arrived jobs and a classification module to classify tasks based on their resource intensiveness. Both the proposed frameworks have been verified theoretically and tested experimentally based on Google Cloud trace logs. Experimental analysis demonstrates the effectiveness of the proposed framework in terms of the achieved reliability of the forecast results and in reducing the server energy expenditures spent towards executing jobs at the datacentres.