With the constant evolution of hardware architecture extensions aimed at enhancing software security, a notable availability gap arises due to the proprietary nature and design-specific characteristics of these features, resulting in a CPU-specific implementation. This gap particularly affects low-end embedded devices that often rely on CPU cores with limited resources. Addressing this challenge, this thesis focuses on providing access to hardware-based Trusted Execution Environments (TEEs) for devices lacking TEE support. RSGX is a framework crafted to transparently offload security-sensitive workloads to an enclave hosted in a remote centralized edge server. Operating as clients, low-end TEE-lacking devices can harness the hardware security features provided by TEEs of either the same or different architecture. RSGX is tailored to accommodate applications developed with diverse TEE-utilizing SDKs, such as the Open Enclave SDK, Intel SGX SDK, and many others. This facilitates easy integration of existing enclave-based applications, and the framework allows users to utilize its features without requiring any source code modifications, ensuring transparent offloading behind the scenes. For the evaluation, we set up an edge computing environment to execute C/C++ applications, including two overhead micro-benchmarks and four popular open-source applications. This evaluation of RSGX encompasses an analysis of its security benefits and a measurement of its performance overhead. We demonstrate that RSGX has the potential to mitigate a range of Common Vulnerability Exposures (CVEs), ensuring the secure execution of confidential computations on hybrid and distributed machines with an acceptable performance overhead. / Master of Science / A vast amount of data is generated globally every day, most of which contains critical information and is often linked to individuals. Therefore, safeguarding data is essential at every stage, whether it's during transmission, storage, or processing. Different security principles are applied to protect data at various stages. This thesis particularly focuses on data in use. To protect data in use, several technologies are available, and one of them is confidential computing, which is a hardware-based security technology. However, confidential computing is limited to certain high-end computing machines, and many resource-constrained devices do not support it. In this thesis, we propose RSGX, a framework to offload secured computation to a confidential computing-capable remote device with a Security as a Service (SECaaS) approach. Through RSGX, users can leverage confidential computing capabilities for any of their applications based on any SDK. RSGX provides this capability transparently and securely. Our evaluation shows that users, by adapting RSGX, can mitigate several security vulnerabilities, thereby enhancing security with a reasonable overhead.

Mobile computation offloading (MCO) is a way of improving mobile device (MD) performance by offloading certain task executions to a more resourceful edge server (ES), rather than running them locally on the MD. This thesis first considers the problem of assigning the wireless communication bandwidth and the ES capacity needed for this remote task execution, so that task completion time constraints are satisfied. The objective is to minimize the average power consumption of the MDs, subject to a cost budget constraint on communication and computation resources. The thesis includes contributions for both soft and hard task completion deadline constraints. The soft deadline case aims to create assignments so that the probability of task completion time deadline violation does not exceed a given violation threshold. In the hard deadline case, it creates resource assignments where task completion time deadlines are always satisfied. The problems are first formulated as mixed integer nonlinear programs. Approximate solutions are then obtained by decomposing the problems into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate the quality of the proposed solutions, which can achieve near optimum performance over a wide range of system parameters. The thesis then introduces algorithms for static task class partitioning in MCO. The objective is to partition a given set of task classes into two sets that are either executed locally or those classes that are permitted to contend for remote ES execution. The goal is to find the task class partition that gives the minimum mean MD power consumption subject to task completion deadlines. The thesis generates these partitions for both soft and hard task completion deadlines. Two variations of the problem are considered. The first assumes that the wireless and computational capacities are given and the second generates both capacity assignments subject to an additional resource cost budget constraint. Two class ordering methods are introduced, one based on a task latency criterion, and another that first sorts and groups classes based on a mean power consumption criterion and then orders the task classes within each group based on a task completion time criterion. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions. The thesis then considers the use of digital twins (DTs) to offload physical system (PS) activity. Each DT periodically communicates with its PS, and uses these updates to implement features that reflect the real behaviour of the device. A given feature can be implemented using different models that create the feature with differing levels of system accuracy. The objective is to maximize the minimum feature accuracy for the requested features by making appropriate model selections subject to wireless channel and ES resource availability. The model selection problem is first formulated as an NP-complete integer program. It is then decomposed into multiple subproblems, each consisting of a modified Knapsack problem. A polynomial-time approximation algorithm is proposed using dynamic programming to solve it efficiently, by violating its constraints by at most a given factor. A generalization of the model selection problem is then given and the thesis proposes an approximation algorithm using dependent rounding to solve it efficiently with guaranteed constraint violations. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions. / Thesis / Doctor of Philosophy (PhD) / Mobile devices (MDs) such as smartphones are currently used to run a wide variety of application tasks. An alternative to local task execution is to arrange for some MD tasks to be run on a remote non-mobile edge server (ES). This is referred to as mobile computation offloading (MCO). The work in this thesis studies two important facets of the MCO problem. 1. The first considers the joint effects of communication and computational resource assignment on task completion times. This work optimizes task offloading decisions, subject to task completion time requirements and the cost that one is willing to incur when designing the network. Procedures are proposed whose objective is to minimize average mobile device power consumption, subject to these cost constraints. 2. The second considers the use of digital twins (DTs) as a way of implementing mobile computation offloading. A DT implements features that describe its physical system (PS) using models that are hosted at the ES. A model selection problem is studied, where multiple DTs share the execution services at a common ES. The objective is to optimize the feature accuracy obtained by DTs subject to the communication and computation resource availability. The thesis proposes different approximation and decomposition methods that solve these problems efficiently.

The limited energy and computing power of small smart devices restricts their ability to support a wide range of applications, especially those needing quick responses. Mobile edge computing offers a potential solution by providing computing resources at the network access points that can be shared by the devices. This enables the devices to offload some of their computational tasks to the access points. To make this work well for multiple devices, we need to judiciously allocate the available communication and computing resources among the devices. The main focus of this thesis is on (near) optimal resource allocation in a K-user offloading system that employs the time division multiple access (TDMA) scheme. In this thesis, we develop effective algorithms for the resource allocation problem that aim to minimize the overall (cost of the) energy that the devices consume in completing their computational tasks within the specified deadlines while respecting the devices' constraints. This problem is tackled for tasks that cannot be divided and hence the system must make a binary decision as to whether or not a task should be offloaded. This implies the need to develop an effective decision-making algorithm to identify a suitable group of devices for offloading. This thesis commences by developing efficient communication resource algorithms that incorporate the impact of integer finite block length in low-latency computational offloading systems with reserved computing resources. In particular, it addresses the challenge of minimizing total energy consumption in a binary offloading scenario involving K users. The approach considers different approximations of the fundamental rate limit in the finite block length regime, departing from the conventional asymptotic rate limits developed by Shannon. Two such alternatives, namely the normal approximation and the SNR-gap approximation, are explored. A decomposition approach is employed, dividing the problem into an inner component that seeks an optimal solution for the communication resource allocation within a defined set of offloading devices, and an outer component aimed at identifying a suitable set of offloading devices. Given the finiteness of the block length and its integer nature, various relaxation techniques are employed to determine an appropriate communication resource allocation. These include incremental and independent roundings, alongside an extended search that utilizes randomization-based methods in both rounding schemes. The findings reveal that incremental randomized rounding, when applied to the normal approximation of the rate limits, enhances system performance in terms of reducing the energy consumption of the offloading users. Furthermore, customized pruned greedy search techniques for selecting the offloading devices efficiently generate good decisions. Indeed, the proposed approach outperforms a number of existing approaches. In the second contribution, we develop efficient algorithms that address the challenge of jointly allocating both computation and communication resources in a binary offloading system. We employ a similar decomposition methodology as in the previous work to perform the decision-making, but this is now done along with joint computation and communication resource allocation. For the inner resource allocation problem, we divide the problem into two components: determining the allocation of computation resources and the optimal allocation of communication resources for the given allocation of computation resources. The allocation of the computation resources implicitly determines a suitable order for data transmission, which facilitates the subsequent optimal allocation of the communication resources. In this thesis, we introduce two heuristic approaches for allocating the computation resources. These approaches sequentially maximize the allowable transmission time for the devices in sequence, starting from the largest leading to a reduction in total offloading energy. We demonstrate that the proposed heuristics substantially lower the computational burden associated with solving the joint computation--communication resource allocation problem while maintaining a low total energy. In particular, its use results in substantially lower energy consumption than other simple heuristics. Additionally, the heuristics narrow the energy gap in comparison to a fictitious scenario in which each task has access to the whole computation resource without the need for sharing. / Thesis / Master of Applied Science (MASc)

The limited energy and computational resources in small-scale smart devices impede the expansion of the range of applications that those devices can support, especially to applications with tight latency constraints. Mobile edge computing is a promising framework that provides shared computational resources in the access points in the network and provides devices in that network with the opportunity to offload (a portion of) their computational tasks to the access points. To effectively capture that opportunity in an offloading system with multiple devices, the available communication and computation resources must be efficiently allocated. The main focus of this thesis is on the optimal allocation of communication resources in a K-user offloading system. The resource allocation problem that is considered in this thesis captures minimizing the total energy consumption of users while the requirements of the users, and their computational tasks, are met. That problem is addressed for two of the most widely-considered classes of computational tasks in the literature, namely, indivisible tasks (binary offloading) and divisible tasks (partial offloading). This thesis begins with an exploration of the impact of the choice of multiple access scheme that is employed by the system on the total energy consumption of the users. In particular, the problem of minimizing the total energy consumption of a two-user binary offloading system is tackled under various multiple access schemes, namely time division multiple access (TDMA), sequential decoding without time sharing, independent decoding, and multiple access schemes that can exploit the full capabilities of the channel, which are referred to as full multiple access schemes (FullMA) in this thesis. Using a decomposition-based approach, closed-form solutions to the resource allocation problem are obtained. Those expressions show that by exploiting the full capabilities of the channel, a FullMA scheme can significantly reduce the total energy consumption of the users as compared to the other schemes. The closed-form expressions also show that when the channel gains of the two users are equal, the TDMA scheme can achieve the optimal energy consumption. For the case of partial offloading, an analogous analysis leads to a reduced-dimension design problem and an extension to the optimally result for TDMA. In the next step of the development, the insights obtained from the decomposition-based analysis of the two-user case are used to tackle the communication resource allocation problem for a K-user offloading system in which the users are assumed to be served over a single time slot. Based on their performance in the two-user case, FullMA and TDMA schemes are considered. The mixed-integer optimization problem that arises in the binary offloading case is addressed by employing a decomposition approach with a closed-form expression obtained for the optimal resource allocation for given offloading decisions, and a tailored pruned greedy search algorithm developed herein for the offloading decisions. By exploiting the maximum allowable latency of each individual user, the proposed algorithm is able to significantly reduce the energy consumption of the users in comparison to the existing algorithms in the literature that assume equal latency constraints for all users. Furthermore, with the closed-form optimal solution to the resource allocation problem obtained for given offloading decisions, the proposed algorithm has a significantly lower computational cost compared to the existing algorithms. In the partial offloading case, a quasi-closed- form solution is obtained for the resource allocation problem. Finally, a time-slotted signalling structure is proposed as an optimal transmission structure for a generic K-user offloading system. Furthermore, an optimal times-lotted structure that requires only K time slots is developed for a K-user offloading system that employs a FullMA scheme. The proposed time-slotted structure not only exploits the maximum latency constraint of each user, it also exploits the differences between the latency constraints of the users by taking advantage of the interference reduction that arises when a user finishes offloading. The proposed time-slotted FullMA signalling structure significantly reduces the energy consumption of the users compared to some existing methods that employ the TDMA scheme, and compared to those with FullMA, but sub-optimal single-time-slot signalling structures. Moreover, the computational cost of the proposed time-slotted algorithm is significantly lower than that of the existing algorithms in the literature. / Dissertation / Doctor of Philosophy (PhD) / The rapid increase in the number of smart devices in wireless communication networks, and the expansion in the range of computationally-intensive and latency sensitive applications that those devices are required to support, have highlighted their resource limitations in terms of energy, power, central processing unit (CPU), and memory. Mobile edge computing is a framework that provides shared computational resources at the access points of wireless networks and gives such devices the opportunity to offload (a portion of) their applications to be executed at the access points. In order to fully exploit such an opportunity when multiple devices seek to offload their applications, the available communication and computation resources must be efficiently allocated amongst those devices. The ultimate goal of this thesis is to obtain the optimal communication resource allocation in a K-user offloading system while different constraints on the devices and on the applications are satis ed. To that end, this thesis shows that the minimum energy consumption is obtained when the system exploits the full capabilities of the channel, the maximum allowable latency of each user, and the differences between the latency constraints of each user. Accordingly, this thesis proposes an optimized signalling structure and, based on that structure, low-complexity algorithms that achieve an energy-optimal resource allocation in a K-user offloading system.

Hardware-acceleration techniques continue to be used to boost the performance of scientific codes. To do so, software developers identify portions of these codes that are amenable for offloading and map them to hardware accelerators. However, offloading such tasks to specialized hardware accelerators is non-trivial. Furthermore, these accelerators can add significant cost to a computing system. Consequently, this thesis proposes a framework called GePSeA (General Purpose Software Acceleration Framework), which uses a small fraction of the computational power on multi-core architectures to offload complex application-specific tasks. Specifically, GePSeA provides a lightweight process that acts as a helper agent to the application by executing application-specific tasks asynchronously and efficiently. GePSeA is not meant to replace hardware accelerators but to extend them. GePSeA provide several utilities called core components that offload tasks on to the core or to the special-purpose hardware when available in a way that is transparent to the application. Examples of such core components include reliable communication service, distributed lock management, global memory management, dynamic load distribution and network protocol processing. We then apply the GePSeA framework to two applications, namely mpiBLAST, an open-source computational biology application and Reliable Blast UDP (RBUDP) based file transfer application. We observe significant speed-up for both applications. / Master of Science

Le délestage de calcul ou de code est une technique qui permet à un appareil mobile avec une contrainte de ressources d'exécuter à distance, entièrement ou partiellement, une application intensive en calcul dans un environnement Cloud avec des ressources suffisantes. Le délestage de code est effectué principalement pour économiser de l'énergie, améliorer les performances, ou en raison de l'incapacité des appareils mobiles à traiter des calculs intensifs. Plusieurs approches et systèmes ont été proposés pour délester du code dans le Cloud tels que CloneCloud, MAUI et Cyber Foraging. La plupart de ces systèmes offrent une solution complète qui traite différents objectifs. Bien que ces systèmes présentent en général de bonnes performances, un problème commun entre eux est qu'ils ne sont pas adaptés aux applications temps réel telles que les jeux vidéo, la réalité augmentée et la réalité virtuelle, qui nécessitent un traitement particulier. Le délestage de code a connu un récent engouement avec l'avènement du MEC et son évolution vers le edge à multiple accès qui élargit son applicabilité à des réseaux hétérogènes comprenant le WiFi et les technologies d'accès fixe. Combiné avec l'accès mobile 5G, une pléthore de nouveaux services mobiles apparaîtront, notamment des service type URLLC et eV2X. De tels types de services nécessitent une faible latence pour accéder aux données et des capacités de ressources suffisantes pour les exécuter. Pour mieux trouver sa position dans une architecture 5G et entre les services 5G proposés, le délestage de code doit surmonter plusieurs défis; la latence réseau élevée, hétérogénéité des ressources, interopérabilité des applications et leur portabilité, la consommation d'énergie, la sécurité, et la mobilité, pour citer quelques uns. Dans cette thèse, nous étudions le paradigme du délestage de code pour des applications a temps réel, par exemple; les jeux vidéo sur équipements mobiles et le traitement d'images. L'accent sera mis sur la latence réseau, la consommation de ressources, et les performances accomplies. Les contributions de la thèse sont organisées sous les axes suivants : Étudier le comportement des moteurs de jeu sur différentes plateformes en termes de consommation de ressources (CPU / GPU) par image et par module de jeu ; Étudier la possibilité de distribuer les modules du moteur de jeu en fonction de la consommation de ressources, de la latence réseau, et de la dépendance du code ; Proposer une stratégie de déploiement pour les fournisseurs de jeux dans le Cloud, afin de mieux exploiter les ressources, en fonction de la demande variable en ressource par des moteurs de jeu et de la QoE du joueur ; Proposer une solution de délestage statique de code pour les moteurs de jeu en divisant la scène 3D en différents objets du jeu. Certains de ces objets sont distribués en fonction de la consommation de ressources, de la latence réseau et de la dépendance du code ; Proposer une solution de délestage dynamique de code pour les moteurs de jeu basée sur une heuristique qui calcule pour chaque objet du jeu, le gain du délestage. En fonction de ce gain, un objet peut être distribué ou non ; Proposer une nouvelle approche pour le délestage de code vers le MEC en déployant une application sur la bordure du réseau (edge) responsable de la décision de délestage au niveau du terminal et proposer deux algorithmes pour prendre la meilleure décision concernant les tâches à distribuer entre le terminal et le serveur hébergé dans le MEC. / Computation offloading is a technique that allows resource-constrained mobile devices to fully or partially offload a computation-intensive application to a resourceful Cloud environment. Computation offloading is performed mostly to save energy, improve performance, or due to the inability of mobile devices to process a computation heavy task. There have been a numerous approaches and systems on offloading tasks in the classical Mobile Cloud Computing (MCC) environments such as, CloneCloud, MAUI, and Cyber Foraging. Most of these systems are offering a complete solution that deal with different objectives. Although these systems present in general good performance, one common issue between them is that they are not adapted to real-time applications such as mobile gaming, augmented reality, and virtual reality, which need a particular treatment. Computation offloading is widely promoted especially with the advent of Mobile Edge Computing (MEC) and its evolution toward Multi-access Edge Computing which broaden its applicability to heterogeneous networks including WiFi and fixed access technologies. Combined with 5G mobile access, a plethora of novel mobile services will appear that include Ultra-Reliable Low-latency Communications (URLLC) and enhanced Vehicle-toeverything (eV2X). Such type of services requires low latency to access data and high resource capabilities to compute their behaviour. To better find its position inside a 5G architecture and between the offered 5G services, computation offloading needs to overcome several challenges; the high network latency, resources heterogeneity, applications interoperability and portability, offloading frameworks overhead, power consumption, security, and mobility, to name a few. In this thesis, we study the computation offloading paradigm for real-time applications including mobile gaming and image processing. The focus will be on the network latency, resource consumption, and accomplished performance. The contributions of the thesis are organized on the following axes : Study game engines behaviour on different platforms regarding resource consumption (CPU/GPU) per frame and per game module; study the possibility to offload game engine modules based on resource consumption, network latency, and code dependency ; propose a deployment strategy for Cloud gaming providers to better exploit their resources based on the variability of the resource demand of game engines and the QoE ; propose a static computation offloading-based solution for game engines by splitting 3D world scene into different game objects. Some of these objects are offloaded based on resource consumption, network latency, and code dependency ; propose a dynamic offloading solution for game engines based on an heuristic that compute for each game object, the offloading gain. Based on that gain, an object may be offloaded or not ; propose a novel approach to offload computation to MEC by deploying a mobile edge application that is responsible for driving the UE decision for offloading, as well as propose two algorithms to make best decision regarding offloading tasks on UE to a server hosted on the MEC.

La computación en nube es una de las tecnologías que están dando forma al mundo actual. En este sentido, las empresas deben hacer uso de esta tecnología para seguir siendo competitivas en un mercado globalizado. Los sectores tradicionales de la industria manufacturera (calzado, muebles, juguetes, entre otros) se caracterizan principalmente por tener un diseño intensivo y un trabajo de fabricación en la producción de nuevos productos de temporada. Este trabajo se realiza a través de software de modelado y fabricación 3D. Este software se conoce habitualmente como “CAD/CAM”. Se basa principalmente en la aplicación de primitivas de modelado y cálculo geométrico. La externalización de procesamiento es el método utilizado para externalizar la carga de procesamiento a la nube. Esta técnica aporta muchas ventajas a los procesos de diseño y fabricación: reducción del coste inicial para pequeñas y medianas empresas que necesitan una gran capacidad de cálculo, infraestructura muy flexible para proporcionar potencia de cálculo ajustable, prestación de servicios informáticos “CAD/CAM” a diseñadores de todo el mundo, etc.. Sin embargo, la externalización del cálculo geométrico a la nube implica varios retos que deben superarse para que la propuesta sea viable. El objetivo de este trabajo es explorar nuevas formas de aprovechar los dispositivos especializados y mejorar las capacidades de las “GPUs” mediante la revisión y comparación de las técnicas de programación paralela disponibles, y proponer la configuración óptima de la arquitectura “Cloud” y el desarrollo de aplicaciones para mejorar el grado de paralelización de los dispositivos de procesamiento especializados, sirviendo de base para su mayor explotación en la nube para pequeñas y medianas empresas. Finalmente, este trabajo muestra los experimentos utilizados para validar la propuesta tanto a nivel de arquitectura de comunicación como de la programación en las "GPU" y aporta unas conclusiones derivadas de esta experimentación.

Currently, smart mobile devices are used for more than just calling and texting. They can run complex applications such as GPS, antivirus, and photo editor applications. Smart devices today offer mobility, flexibility, and portability, but they have limited resources and a relatively weak battery. As companies began creating mobile resource intensive and power intensive applications, they have realized that cloud computing was one of the solutions that they could utilize to overcome smart device constraints. Cloud computing helps decrease memory usage and improve battery life. Mobile cloud computing is a current and expanding research area focusing on methods that allow smart mobile devices to take full advantage of cloud computing. Code offloading is one of the techniques employed in cloud computing with mobile devices. This research compares two dynamic offloading frameworks to determine which one is better in terms of execution time and battery life improvement.

This thesis defines and applies a methodology for analyzing the possibility of extending the operating sector of DP shuttle tankers for offloading operations in Spread Moored FPSO Platforms. Extending the operating sector is beneficial to increase operations\' availability and to reduce DP power demand under certain environmental conditions. This study is important, since several emergency disconnections have occurred during offloading in Santos Basin because the shuttle tanker was pushed out of the green sector by environmental resultants that pointed to the West. However, this proposal has to be carefully analyzed in order to guarantee the operations\' safety and not to increase the risks of collision between FPSO and shuttle tanker, of oil pollution and of personnel safety. The methodology consists in five basic steps: Preliminary Risk Analysis to assess the potential hazards associated with the new sector; evaluation of the uptime gain through static analysis; evaluation of DP power demand inside the original and extended sector; real time simulations to evaluate the operation in a realistic environment; and, finally, field tests to validate the proposal. This thesis presents the contextualization of the problem, a bibliographical research, theoretical concepts, the detailed methodology and results of each step. The results show that the average uptime gain is significant both in Campos and Santos Basins - up to 9% and 13% respectively - and that the additional risks created by the sector extension are well mitigated if some recommendations are put into place. The conclusion of this thesis is that extending the operating sector is not only beneficial but also safe. / Esta dissertação define e aplica uma metodologia para analisar a possibilidade de extensão do setor operacional de navios aliviadores DP para operações de offloading em plataformas FPSO ancoradas em Spread-Mooring. Esta proposta apresenta como vantagens o aumento da disponibilidade das operações e redução na demanda de energia do navio DP em certas condições ambientais. O estudo é importante tendo em vista que várias desconexões de emergência já ocorreram durante operações de alívio na Bacia de Santos, porque o navio-tanque foi empurrado para fora do setor verde por resultantes ambientais que apontavam para oeste. No entanto, a proposta deve ser cuidadosamente analisada para garantir que o novo setor não aumenta os riscos de colisão, de poluição ambiental e de segurança às pessoas. A metodologia consiste em cinco etapas básicas: Análise Preliminar de Riscos (APR); avaliação do ganho de disponibilidade da operação; avaliação da demanda de energia do sistema DP nos setores original e estendido; simulações de manobra em tempo real; e, finalmente, testes em campo para validação da proposta. Esta dissertação apresenta a contextualização do problema, pesquisa bibliográfica, conceitos teóricos, a metodologia detalhada e os resultados de cada etapa. Os resultados mostram que o ganho médio de disponibilidade é significativo nas bacias de Campos e de Santos - até 9% e 13%, respectivamente - e que os riscos adicionais criados pela extensão do setor são devidamente mitigados se as recomendações levantadas na APR forem implementadas. A conclusão desta dissertação é que estender o setor operacional é, não somente benéfico, mas também seguro.

This thesis defines and applies a methodology for analyzing the possibility of extending the operating sector of DP shuttle tankers for offloading operations in Spread Moored FPSO Platforms. Extending the operating sector is beneficial to increase operations\' availability and to reduce DP power demand under certain environmental conditions. This study is important, since several emergency disconnections have occurred during offloading in Santos Basin because the shuttle tanker was pushed out of the green sector by environmental resultants that pointed to the West. However, this proposal has to be carefully analyzed in order to guarantee the operations\' safety and not to increase the risks of collision between FPSO and shuttle tanker, of oil pollution and of personnel safety. The methodology consists in five basic steps: Preliminary Risk Analysis to assess the potential hazards associated with the new sector; evaluation of the uptime gain through static analysis; evaluation of DP power demand inside the original and extended sector; real time simulations to evaluate the operation in a realistic environment; and, finally, field tests to validate the proposal. This thesis presents the contextualization of the problem, a bibliographical research, theoretical concepts, the detailed methodology and results of each step. The results show that the average uptime gain is significant both in Campos and Santos Basins - up to 9% and 13% respectively - and that the additional risks created by the sector extension are well mitigated if some recommendations are put into place. The conclusion of this thesis is that extending the operating sector is not only beneficial but also safe. / Esta dissertação define e aplica uma metodologia para analisar a possibilidade de extensão do setor operacional de navios aliviadores DP para operações de offloading em plataformas FPSO ancoradas em Spread-Mooring. Esta proposta apresenta como vantagens o aumento da disponibilidade das operações e redução na demanda de energia do navio DP em certas condições ambientais. O estudo é importante tendo em vista que várias desconexões de emergência já ocorreram durante operações de alívio na Bacia de Santos, porque o navio-tanque foi empurrado para fora do setor verde por resultantes ambientais que apontavam para oeste. No entanto, a proposta deve ser cuidadosamente analisada para garantir que o novo setor não aumenta os riscos de colisão, de poluição ambiental e de segurança às pessoas. A metodologia consiste em cinco etapas básicas: Análise Preliminar de Riscos (APR); avaliação do ganho de disponibilidade da operação; avaliação da demanda de energia do sistema DP nos setores original e estendido; simulações de manobra em tempo real; e, finalmente, testes em campo para validação da proposta. Esta dissertação apresenta a contextualização do problema, pesquisa bibliográfica, conceitos teóricos, a metodologia detalhada e os resultados de cada etapa. Os resultados mostram que o ganho médio de disponibilidade é significativo nas bacias de Campos e de Santos - até 9% e 13%, respectivamente - e que os riscos adicionais criados pela extensão do setor são devidamente mitigados se as recomendações levantadas na APR forem implementadas. A conclusão desta dissertação é que estender o setor operacional é, não somente benéfico, mas também seguro.