The adoption of cloud computing has increased dramatically in recent years due to at- tractive features such as flexibility, cost reductions, scalability, and pay per use. Shifting towards cloud computing is attracting not only industry but also government and academia. However, given their stringent privacy and security policies, this shift is still hindered by many security concerns related to the cloud computing features, namely shared resources, virtualization and multi-tenancy. These security concerns vary from privacy threats and lack of transparency to intrusions from within and outside the cloud infrastructure. There- fore, to overcome these concerns and establish a strong trust in cloud computing, there is a need to develop adequate security mechanisms for effectively handling the threats faced in the cloud. Intrusion Detection Systems (IDSs) represent an important part of such mech- anisms. Developing cloud based IDS that can capture suspicious activity or threats, and prevent attacks and data leakage from both inside and outside the cloud environment is paramount. However, cloud computing is faced with a multidimensional and rapidly evolv- ing threat landscape, which makes cloud based IDS more challenging. Moreover, one of the most significant hurdles for developing such cloud IDS is the lack of publicly available datasets collected from a real cloud computing environment. In this dissertation, we intro- duce the first public dataset of its kind, named ISOT Cloud Intrusion Dataset (ISOT-CID), for cloud intrusion detection. The dataset consists of several terabytes of data, involving normal activities and a wide variety of attack vectors, collected over multiple phases and periods of time in a real cloud environment. We also introduce a new hypervisor-based cloud intrusion detection system (HIDS) that uses online multivariate statistical change analysis to detect anomalous network behaviors. As a departure from the conventional monolithic network IDS feature model, we leverage the fact that a hypervisor consists of a collection of instances, to introduce an instance-oriented feature model that exploits indi- vidual as well as correlated behaviors of instances to improve the detection capability. The proposed approach is evaluated using ISOT-CID and the experiments along with results are presented. / Graduate / 2020-08-14

De nos jours, de nombreuses entreprises choisissent de plus en plus d'adopter le cloud computing. Plus précisément, en tant que clients, elles externalisent la gestion de leur infrastructure physique vers des centres de données (ou plateformes de cloud computing). La consommation d'énergie est une préoccupation majeure pour la gestion des centres de données (datacenter, DC). Son impact financier représente environ 80% du coût total de possession et l'on estime qu'en 2020, les DCs américains dépenseront à eux seuls environ 13 milliards de dollars en factures énergétiques. Généralement, les serveurs de centres de données sont conçus de manière à atteindre une grande efficacité énergétique pour des utilisations élevées. Pour diminuer le coût de calcul, les serveurs de centre de données devraient maximiser leur utilisation. Afin de lutter contre l'utilisation historiquement faible des serveurs, le cloud computing a adopté la virtualisation des serveurs. Cette dernière permet à un serveur physique d'exécuter plusieurs serveurs virtuels (appelés machines virtuelles) de manière isolée. Avec la virtualisation, le fournisseur de cloud peut regrouper (consolider) l'ensemble des machines virtuelles (VM) sur un ensemble réduit de serveurs physiques et ainsi réduire le nombre de serveurs actifs. Même ainsi, les serveurs de centres de données atteignent rarement des utilisations supérieures à 50%, ce qui signifie qu'ils fonctionnent avec des ensembles de ressources majoritairement inutilisées (appelés «trous»). Ma première contribution est un système de gestion de cloud qui divise ou fusionne dynamiquement les machines virtuelles de sorte à ce qu'elles puissent mieux remplir les trous. Cette solution n'est efficace que pour des applications élastiques, c'est-à-dire des applications qui peuvent être exécutées et reconfigurées sur un nombre arbitraire de machines virtuelles. Cependant, la fragmentation des ressources provient d'un problème plus fondamental. On observe que les applications cloud demandent de plus en plus de mémoire, tandis que les serveurs physiques fournissent plus de CPU. Dans les DC actuels, les deux ressources sont fortement couplées puisqu'elles sont liées à un serveur physique. Ma deuxième contribution est un moyen pratique de découpler la paire CPU-mémoire, qui peut être simplement appliquée à n'importe quel serveur. Ainsi, les deux ressources peuvent varier indépendamment, en fonction de leur demande. Ma troisième et ma quatrième contribution montrent un système pratique qui exploite la deuxième contribution. La sous-utilisation observée sur les serveurs physiques existe également pour les machines virtuelles. Il a été démontré que les machines virtuelles ne consomment qu'une petite fraction des ressources allouées car les clients du cloud ne sont pas en mesure d'estimer correctement la quantité de ressources nécessaire à leurs applications. Ma troisième contribution est un système qui estime la consommation de mémoire (c'est-à-dire la taille du working set) d'une machine virtuelle, avec un surcoût faible et une grande précision. Ainsi, nous pouvons maintenant consolider les machines virtuelles en fonction de la taille de leur working set (plutôt que leur mémoire réservée). Cependant, l'inconvénient de cette approche est le risque de famine de mémoire. Si une ou plusieurs machines virtuelles ont une forte augmentation de la demande en mémoire, le serveur physique peut manquer de mémoire. Cette situation n'est pas souhaitable, car la plate-forme cloud est incapable de fournir au client la mémoire qu'il a payée. Finalement, ma quatrième contribution est un système qui permet à une machine virtuelle d'utiliser la mémoire distante fournie par un autre serveur du rack. Ainsi, dans le cas d'un pic de la demande en mémoire, mon système permet à la VM d'allouer de la mémoire sur un serveur physique distant. / Nowadays, many organizations choose to increasingly implement the cloud computing approach. More specifically, as customers, these organizations are outsourcing the management of their physical infrastructure to data centers (or cloud computing platforms). Energy consumption is a primary concern for datacenter (DC) management. Its cost represents about 80% of the total cost of ownership and it is estimated that in 2020, the US DCs alone will spend about $13 billion on energy bills. Generally, the datacenter servers are manufactured in such a way that they achieve high energy efficiency at high utilizations. Thereby for a low cost per computation all datacenter servers should push the utilization as high as possible. In order to fight the historically low utilization, cloud computing adopted server virtualization. The latter allows a physical server to execute multiple virtual servers (called virtual machines) in an isolated way. With virtualization, the cloud provider can pack (consolidate) the entire set of virtual machines (VMs) on a small set of physical servers and thereby, reduce the number of active servers. Even so, the datacenter servers rarely reach utilizations higher than 50% which means that they operate with sets of longterm unused resources (called 'holes'). My first contribution is a cloud management system that dynamically splits/fusions VMs such that they can better fill the holes. This solution is effective only for elastic applications, i.e. applications that can be executed and reconfigured over an arbitrary number of VMs. However the datacenter resource fragmentation stems from a more fundamental problem. Over time, cloud applications demand more and more memory but the physical servers provide more an more CPU. In nowadays datacenters, the two resources are strongly coupled since they are bounded to a physical sever. My second contribution is a practical way to decouple the CPU-memory tuple that can simply be applied to a commodity server. Thereby, the two resources can vary independently, depending on their demand. My third and my forth contribution show a practical system which exploit the second contribution. The underutilization observed on physical servers is also true for virtual machines. It has been shown that VMs consume only a small fraction of the allocated resources because the cloud customers are not able to correctly estimate the resource amount necessary for their applications. My third contribution is a system that estimates the memory consumption (i.e. the working set size) of a VM, with low overhead and high accuracy. Thereby, we can now consolidate the VMs based on their working set size (not the booked memory). However, the drawback of this approach is the risk of memory starvation. If one or multiple VMs have an sharp increase in memory demand, the physical server may run out of memory. This event is undesirable because the cloud platform is unable to provide the client with the booked memory. My fourth contribution is a system that allows a VM to use remote memory provided by a different rack server. Thereby, in the case of a peak memory demand, my system allows the VM to allocate memory on a remote physical server.

Recently, companies are adopting Mobile Cloud Computing (MCC) to efficiently deliver enterprise services to users (or consumers) on their personalized devices. MCC is the facilitation of mobile devices (e.g., smartphones, tablets, notebooks, and smart watches) to access virtualized services such as software applications, servers, storage, and network services over the Internet. With the advancement and diversity of the mobile landscape, there has been a growing trend in consumer attitude where a single user owns multiple mobile devices. This paradigm of supporting a single user or consumer to access multiple services from n-devices is referred to as the Ubiquitous Cloud Computing (UCC) or the Personal Cloud Computing. In the UCC era, consumers expect to have application and data consistency across their multiple devices and in real time. However, this expectation can be hindered by the intermittent loss of connectivity in wireless networks, user mobility, and peak load demands. Hence, this dissertation presents an architectural framework called, Cloud Services Brokerage for Mobile Ubiquitous Cloud Computing (CSB-UCC), which ensures soft real-time and reliable services consumption on multiple devices of users. The CSB-UCC acts as an application middleware broker that connects the n-devices of users to the multi-cloud services. The designed system determines the multi-cloud services based on the user's subscriptions and the n-devices are determined through device registration on the broker. The preliminary evaluations of the designed system shows that the following are achieved: 1) high scalability through the adoption of a distributed architecture of the brokerage service, 2) providing soft real-time application synchronization for consistent user experience through an enhanced mobile-to-cloud proximity-based access technique, 3) reliable error recovery from system failure through transactional services re-assignment to active nodes, and 4) transparent audit trail through access-level and context-centric provenance.

Le régime juridique applicable à la collecte et à l’exploitation par les fournisseurs de services de cloud computing des données à caractère personnel de leurs utilisateurs constitue une source d’interrogation pour ces derniers. De fait, aucun régime juridique organisé ne permet aujourd’hui de réguler de manière globale, au niveau de l’Union européenne, le phénomène de circulation des données à caractère personnel dans le cloud, que ce soit de manière directe ou indirecte. Il apparaît, dès lors, nécessaire de s’interroger sur la manière dont le droit s’est organisé en conséquence et d’analyser les traitements complémentaires et/ou alternatifs actuellement offerts par le droit, certes moins structurellement organisés et mosaïques, mais plus pragmatiques, réalistes et politiquement viables. Historiquement, le phénomène de circulation a été presque exclusivement traité via le droit spécifique à la protection des données à caractère personnel découlant de l’Union européenne. Ce droit, souvent considéré par opposition au droit à la libre circulation des données, constituait initialement une émanation du droit à la protection de la vie privée avant d’être consacré en tant que droit fondamental de l’Union européenne. Le traitement offert par le droit à la protection des données, s’il cible directement les données au cœur du phénomène de circulation dans le cloud, ne couvre que partiellement ledit phénomène. De surcroît, malgré l’entrée en vigueur du Règlement 2016/679 relatif à la protection des personnes physiques à l’égard du traitement des données à caractère personnel et à la libre circulation de ces données, il possède une efficacité contestable, ne proposant pas de solution harmonisée au sein de l’Union européenne et étant dépendant de la bonne volonté et des moyens financiers, organisationnels et humains des Etats Membres. Les traitements alternatifs ou complémentaires au droit à la protection des données qui existent au sein de l’Union européenne, qui peuvent être répartis entre outils techniques, contractuels et législatifs, n’offrent qu’une appréhension indirecte du phénomène de circulation via un encadrement de son environnement cloud. Individuellement, ils ne permettent d’appréhender qu’un aspect très réduit du phénomène de circulation, de surcroît avec une efficacité plus ou moins grande. En outre, les outils techniques et contractuels n’ont pas la légitimité attachée aux outils législatifs. Néanmoins, associés les uns aux autres, ils permettent de cibler le phénomène de circulation des données de manière plus globale et efficace. / The legal framework applicable to the gathering and processing by cloud service providers of the personal data of their users raises questions for such users. De facto, there does not now exist an organized legal framework allowing for the regulation, at the European Union level and as a whole, of the flow of personal data in the cloud, whether directly or indirectly. It thus seems necessary to question the way law organized itself consequently and analyze the complementary and/or alternative treatments offered by law, which are less structurally organized and are mosaical, but are more pragmatic, realistic and politically sustainable. Historically, the flow of personal data has been dealt almost exclusively via the specific right to the protection of personal data, which derives from the European Union. Such right, often considered in opposition to the right to the free circulation of data, was initially an emanation of the right to privacy before being established as a fundamental right of the European Union. The treatment provided by the right to the protection of personal data, if it targets directly the data within the flow phenomena, only partly covers such phenomena. In addition, despite the entry into force of the Regulation 2016/679 on the protection of individuals with regard to the processing of personal data and on the free movement of such data, its effectiveness is questionable, not offering any harmonized solution within the European Union and being highly dependent on the goodwill and the financial, organizational and human means of the Member States. The complementary and/or alternative treatments to the right to the protection of personal data that exist within the European Union, which may be allocated among technical, contractual and regulatory tools, only approach the data flow phenomena indirectly by providing a framework to its environment. Individually, they only target one very limited aspect of the data flow phenomena, with more or less effectiveness. Furthermore, technical and contractual tools have not the legitimacy attached to the regulatory tools. However, associated one with another, they allow a more global and efficient targeting of the data flow phenomena.

The explosion in growth of smart wireless devices has increased the ubiquitous presence of computational resources and location-based data. This new reality of numerous wireless devices capable of collecting, sharing, and processing information, makes possible an avenue for new enhanced applications. Multiple radio nodes with diverse functionalities can form a wireless cloud computing network (WCCN) and collaborate on executing complex applications using wireless distributed computing (WDC). Such a dynamically composed virtual cloud environment can offer services and resources hosted by individual nodes for consumption by user applications. This dissertation proposes an architectural framework for WCCNs and presents the different phases of its development, namely, development of a mathematical system model of WCCNs, simulation analysis of the performance benefits offered by WCCNs, design of decision-making mechanisms in the architecture, and development of a prototype to validate the proposed architecture. The dissertation presents a system model that captures power consumption, energy consumption, and latency experienced by computational and communication activities in a typical WCCN. In addition, it derives a stochastic model of the response time experienced by a user application when executed in a WCCN. Decision-making and resource allocation play a critical role in the proposed architecture. Two adaptive algorithms are presented, namely, a workload allocation algorithm and a task allocation - scheduling algorithm. The proposed algorithms are analyzed for power efficiency, energy efficiency, and improvement in the execution time of user applications that are achieved by workload distribution. Experimental results gathered from a software-defined radio network prototype of the proposed architecture validate the theoretical analysis and show that it is possible to achieve 80 % improvement in execution time with the help of just three nodes in the network. / Ph. D.

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. / He subjects addressed in this thesis are inspired from research problems faced by the Lokad company. These problems are related to the challenge of designing efficient parallelization techniques of clustering algorithms on a Cloud Computing platform. Chapter 2 provides an introduction to the Cloud Computing technologies, especially the ones devoted to intensivecomputations. Chapter 3 details more specifically Microsoft Cloud Computing offer : Windows Azure. The following chapter details technical aspects of cloud application development and provides some cloud design patterns. Chapter 5 is dedicated to the parallelization of a well-known clustering algorithm: the Batch K-Means. It provides insights on the challenges of a cloud implementation of distributed Batch K-Means, especially the impact of communication costs on the implementation efficiency. Chapters 6 and 7 are devoted to the parallelization of another clustering algorithm, the Vector Quantization (VQ). Chapter 6 provides an analysis of different parallelization schemes of VQ and presents the various speedups to convergence provided by them. Chapter 7 provides a cloud implementation of these schemes. It highlights that it is the online nature of the VQ technique that enables an asynchronous cloud implementation, which drastically reducesthe communication costs introduced in Chapter 5.

M. Tech. (Department of Information Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Virtualization is the main technology that powers today’s cloud computing systems. Virtualization provides isolation as well as resource control that enable multiple workloads to run efficiently on a single shared machine and thus allows servers that traditionally require multiple physical machines to be consolidated to a single, cost-effective physical machine using virtual machines or containers. Due to virtual machine techniques, the strategies that improve performance like hardware acceleration, running concurrent virtual machines without the correct proper resource controls not used and correctly configured, the problems of scalability as well as service provisioning (crashing response time, resource contention and functionality or usability) for cloud computing, emanate from the configurations of the virtualized system. Virtualization performance is a critical factor in datacentre and cloud computing service delivery. To evaluate virtualization performance as well as to determine which virtual machine configuration provides effective performance, how to allocate and distribute resources for virtual machine performance equally is critical in this research study. In this study, datacentre purposed servers together with Type 1 (bare metal hypervisors), VMware ESXi 5.5, and Proxmox 5.3 were used to evaluate virtualization performance. The experimental environment was conducted on server Cisco UCS B200 M4 which was the host machine and the virtual environment that is encapsulated within the physical layer which hosts the guest virtual machines consisting of virtual hardware, Guest OSs, and third-party applications. The host server consists of virtual machines with one operating system, CentOS 7 64 bit. For performance evaluation purposes, each guest operating system was configured and allocated the same amount of virtual system resources. Various Workload/benchmarking tools were used for Network, CPU, Memory as well as Disk performance, namely; Iperf, Unibench, Ramspeed, and IOzone, respectively. In the case of Iozone, VMware was more than twice as fast as Proxmox. Although CPU utilization in Proxmox was not noticeably affected, considerably less CPU utilization was observed in VMware. While testing the memory performance with ramspeed, VMware performs 16 to 26% better than Proxmox. In the case of writing, VMware observed 31 to 51% better than Proxmox. In a network, it was observed that the performance on Proxmox was very close to the level of bare metal setup. The results of the performance tests show that the additional operations required by virtualization can be confirmed utilizing test programs. The number of additional operations and their type influence specifically to performance as overhead. In memory and disk areas, where the virtualization procedure was clear, the test outcomes demonstrate that the measure of overhead is little. Processor and network virtualization, then again, was more perplexing. Hence the overhead is more significant. At the point when the overall performance of a virtual machine running in VMware ESXi Server is contrasted with a conventional system, the virtualization causes approximately an increase of 33% in performance.Because of the difficulty in providing optimal real system configurations, workload/benchmarks could provide close to real application systems for better results. The tests demonstrate that virtualization depends immensely on the host system and the virtualization software. Given the tests, both VMware ESXi Server and Proxmox are capable of providing Optimal performance.

volving technology, has kept drawing a significant attention from both the computing industry and academia for nearly a decade.

Cloud computing has become a powerful enabler for many IT services and new technolo-gies. It provides access to an unprecedented amount of resources in a fine-grained andon-demand manner. To deliver such a service, cloud providers should be able to efficientlyand reliably manage their available resources. This becomes a challenge for the manage-ment system as it should handle a large number of heterogeneous resources under diverseworkloads with fluctuations. In addition, it should also satisfy multiple operational require-ments and management objectives in large scale data centers.Autonomic computing techniques can be used to tackle cloud resource managementproblems. An autonomic system comprises of a number of autonomic elements, which arecapable of automatically organizing and managing themselves rather than being managedby external controllers. Therefore, they are well suited for decentralized control, as theydo not rely on a centrally managed state. A decentralized autonomic system benefits fromparallelization of control, faster decisions and better scalability. They are also more reliableas a failure of one will not affect the operation of the others, while there is also a lower riskof having faulty behaviors on all the elements, all at once. All these features are essentialrequirements of an effective cloud resource management.This thesis investigates algorithms, models, and techniques to autonomously managejobs, services, and virtual resources in a cloud data center. We introduce a decentralizedresource management framework, that automates resource allocation optimization and ser-vice consolidation, reliably schedules jobs considering probabilistic failures, and dynam-icly scales and repacks services to achieve cost efficiency.As part of the framework, we introduce a decentralized scheduler that provides andmaintains durable allocations with low maintenance costs for data centers with dynamicworkloads. The scheduler assigns resources in response to virtual machine requests andmaintains the packing efficiency while taking into account migration costs, topologicalconstraints, and the risk of resource contention, as well as fluctuations of the backgroundload.We also introduce a scheduling algorithm that considers probabilistic failures as part ofthe planning for scheduling. The aim of the algorithm is to achieve an overall job reliabil-ity, in presence of correlated failures in a data center. To do so, we study the impacts ofstochastic and correlated failures on job reliability in a virtual data center. We specificallyfocus on correlated failures caused by power outages or failure of network components onjobs running large number of replicas of identical tasks.Additionally, we investigate the trade-offs between vertical and horizontal scaling. Theresult of the investigations is used to introduce a repacking technique to automatically man-age the capacity required by an elastic service. The repacking technique combines thebenefits of both scaling strategies to improve its cost-efficiency. / Datormoln har kommit att bli kraftfulla möjliggörare för många nya IT-tjänster. De ger tillgång till mycket storskaliga datorresurser på ett finkornigt och omedelbart sätt. För att tillhandahålla sådana resurser krävs att de underliggande datorcentren kan hantera sina resurser på ett tillförlitligt och effektivt sätt. Frågan hur man ska designa deras resurshanteringssystem är en stor utmaning då de ska kunna hantera mycket stora mängder heterogena resurser som i sin tur ska klara av vitt skilda typer av belastning, ofta med väldigt stora variationer över tid. Därtill ska de typiskt kunna möta en mängd olika krav och målsättningar för hur resurserna ska nyttjas. Autonomiska system kan med fördel användas för att realisera sådana system. Ett autonomt system innehåller ett antal autonoma element som automatiskt kan organisera och hantera sig själva utan stöd av externa regulatorer. Förmågan att hantera sig själva gör dem mycket lämpliga som komponenter i distribuerade system, vilka i sin tur kan bidra till snabbare beslutsprocesser, bättre skalbarhet och högre feltolerans. Denna avhandling fokuserar på algoritmer, modeller och tekniker för autonom hantering av jobb och virtuella resurser i datacenter. Vi introducerar ett decentraliserat resurshanteringssystem som automatiserar resursallokering och konsolidering, schedulerar jobb tillförlitligt med hänsyn till korrelerade fel, samt skalar resurser dynamiskt för att uppnå kostnadseffektivitet. Som en del av detta ramverk introducerar vi en decentraliserad schedulerare som allokerar resurser med hänsyn till att tagna beslut ska vara bra för lång tid och ge låga resurshanteringskostnader för datacenter med dynamisk belastning. Scheduleraren allokerar virtuella maskiner utifrån aktuell belastning och upprätthåller ett effektivt nyttjande av underliggande servrar genom att ta hänsyn till migrationskostnader, topologiska bivillkor och risk för överutnyttjande. Vi introducerar också en resursallokeringsalgoritm som tar hänsyn till korrelerade fel som ett led i planeringen. Avsikten är att kunna uppnå specificerade tillgänglighetskrav för enskilda tjänster trots uppkomst av korrelerade fel. Vi fokuserar främst på korrelerade fel som härrör från problem med elförsörjning och från felande nätverkskomponenter samt deras påverkan på jobb bestående av många identiska del-jobb. Slutligen studerar vi även hur man bäst ska kombinera horisontell och vertikal skalning av resurser. Resultatet är en process som ökar kostnadseffektivitet genom att kombinera de två metoderna och därtill emellanåt förändra fördelning av storlekar på virtuella maskiner.

Cloud Computing has started to become a viable option for computing centers and mobile consumers seeking to reduce cost overhead, power consumption, and increase software services available within their platform. For instance distributed memory constrained mobile devices can expand their ability to share real time data by utilizing virtual memory located within the cloud. Cloud memory services can be configured to restrict read and write access to the shared memory pool on a partner by partner basis. Utilization of such resources in turn reduces hardware requirements on mobile devices while lessening power consumption for each physical resource. Within the Cloud Computing paradigm, computing resources are provisioned to consumers on demand and guaranteed through service level agreements. Although the idea of a computing utility is not new, its realization has come to pass as researchers and corporate companies embark on a journey of implementing highly scalable cloud environments. As new solutions and architectures are proposed, additional use cases and consumer concerns have been revealed. These issues range from consumer security, adequate service level agreements and vendor interoperability, to cloud technology standardizations. Further, the current state of the art does not adequately address these needs for mobile consumers, where services need to be guaranteed even as consumers dynamically change locations. Due to the rapid adoption of virtualization stacks and the dramatic increase of mobile computing devices, cloud providers must be able to handle logical and physical mobility of consumers. As consumers move throughout geographical regions, there exists the probability that a consumer’s new locale may hinder a producer’s ability to uphold service level agreements. This inability is due to the fact that a producer may not have physical resources located relatively close to a mobile consumer’s new locale. As a consequence, producers must either continue to provide degraded resource consumption or migrate workloads to third party producers in order to ensure service level agreements are maintained. The goal of this report is to research existing architectures that provide the ability to adequately uphold service level agreements as mobile consumers move from locale to locale. Further we propose an architecture that can be implemented along with existing solutions in order to ensure consumers receive adequate service levels regardless of locality. We believe this architecture will lead to increased cloud interoperability and decreased consumer to producer platform coupling. / text