Para explorar campos de petróleo offshore em águas profundas, o uso de plataformas offshore (FPSO - do inglês Floating Production Storage and Offloading) e navios aliviadores, nas últimas décadas, tornou-se uma alternativa economicamente e tecnicamente viável. A FPSO é um tipo de navio petroleiro transformado para a exploração, e armazenamento petróleo. O escoamento da produção é usualmente realizado por um navio tanque aliviador, conectado em tandem, ou por dutos. Porém o transporte marítimo realizado pelos navios petroleiros está sendo cada vez mais aceito, chegando a ser o principal meio para escoar a produção em águas profundas. Entretanto, como contrapartida desta viabilidade técnica, passou-se a executar operações de transbordo entre unidades flutuantes em ambientes mais agressivos, causando um aumento do risco associado com estas operações. Este trabalho, visando garantir a segurança das operações de transferência em alto mar, apresenta a aplicação de técnicas de análise de risco para a avaliação de operações de offloading entre unidades de produção tipo FPSO e navios aliviadores. É aplicado um método indutivo para a identificação de riscos baseado no princípio de que os acidentes acontecem como consequência do desenvolvimento de um evento de perigo durante a operação, que pode durar cerca de 24 horas. No contexto deste trabalho de pesquisa, a análise de risco é entendida como quatro processos sequenciais: (i) identificação dos cenários de perigos, (ii) estimação da probabilidade de ocorrência de falhas para cada cenário, (iii) avaliação das consequências, e, (iv) tomada de decisão. Para melhorar a avaliação da probabilidade é proposto o uso de técnicas bayesianas. Para fazer uma análise mais abrangente das consequências de falha é proposto utilizar o processo markoviano para modelar a probabilidade de mudanças do sistema FPSO-navio aliviador durante a operação de offloading que podem causar mudanças no perfil de risco. A tomada de decisão é usada para avaliar a possibilidade de desconexão de emergência durante a operação. O método é aplicado para avaliar o risco de uma operação de offloading na Bacia de Campos (Brasil), entre uma plataforma tipo FPSO e um navio aliviador tipo Suezmax. Verifica-se que as condições ambientais e a forma de realização da amarração exercem significativa influencia no perfil de risco. / To explore offshore oil fields in deep water the use of a Floating Production Storage and Offloading (FPSO) unit coupled to a shuttle tanker is economically and technically feasible. The FPSO unit normally consists of a ship shaped hull, with an internal or external turret, and production equipment on the deck. The unit is also equipped for crude oil storage. Oil transportation systems required for supporting this infrastructure are pipelines or shuttle tankers. Shuttle tankers are increasingly being accepted as a preferred transportation method for remote and deepwater offshore developments. The offloading operation is considered one of the most risky operations in offshore environments. This dissertation presents a risk-based analysis method aiming at defining the risk profile associated with an offloading operation. For offloading operations the risk profile is usually evaluated considering that the environmental condition could suffer changes during offloading that has an approximate duration of 24 hours. The method follows four basic steps: identification of hazard (using PHA technique), definition of failure scenarios and their probability of occurrence (using cause-consequence diagram and FTA) and evaluation of failure consequences. To improve the evaluation of failure consequences a more comprehensive analysis is proposed aiming at using the Markovian process to model the probability of changes during offloading operation that could cause changes in the risk profile developed in step two. The decision method is used to evaluate the possibility of emergency disconnection during the operation. The method is applied to evaluate the risk profile of an offloading operation in Campos Basin, Brazil, considering o FPSO. The analysis shows that the environmental conditions and the way that the tanker is moored have great influence on the risk profile.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / Os dispositivos mÃveis, especificamente os smartphones e os tablets, evoluÃram bastante em termos computacionais nos Ãltimos anos, e estÃo cada vez mais presentes no cotidiano das pessoas. Apesar dos avanÃos tecnolÃgicos, a principal limitaÃÃo desses dispositivos està relacionada com a questÃo energÃtica e com seu baixo desempenho computacional, quando comparado com um notebook ou computador de mesa. Com base nesse contexto, surgiu o paradigma do Mobile Cloud Computing (MCC), o qual estuda formas de estender os recursos computacionais e energÃticos dos dispositivos mÃveis atravÃs da utilizaÃÃo das tÃcnicas de offloading. A partir do levantamento bibliogrÃfico dos frameworks em MCC verificou-se, para o problema da heterogeneidade em plataformas mÃveis, ausÃncia de soluÃÃes de offloading. Diante deste problema, esta dissertaÃÃo apresenta um framework denominado de MpOS (Multiplataform Offloading System), que suporta a tÃcnica de offloading, em relaÃÃo ao desenvolvimento de aplicaÃÃes para diferentes plataformas mÃveis, sendo desenvolvido inicialmente para as plataformas Android e Windows Phone. Para validaÃÃo foram desenvolvidas para cada plataforma mÃvel, duas aplicaÃÃes mÃveis, denominadas de BenchImage e Collision, que demonstram o funcionamento da tÃcnica de offloading em diversos cenÃrios. No caso do experimento realizado com BenchImage foi analisado o desempenho da aplicaÃÃo mÃvel, em relaÃÃo à execuÃÃo local, no cloudlet server e em uma nuvem pÃblica na Internet, enquanto no experimento do Collision (um aplicativo de tempo real) foi analisado o desempenho do offloading, utilizando tambÃm diferentes sistemas de serializaÃÃo de dados. Em ambos os experimentos houve situaÃÃes que era mais vantajoso executar localmente no smartphone, do que realizar a operaÃÃo de offloading e vice-versa, por causa de diversos fatores associados com a qualidade da rede e com volume de processamento exigido nesta operaÃÃo. / The mobile devices, like smartphones and tablets, have evolved considerably in last years in computational terms. Despite advances in their hardware, these devices have energy constraints regarded to their poor computing performance. Therefore, on this context, a new paradigm called Mobile Cloud Computing (MCC) has emerged. MCC studies new ways to extend the computational and energy resources, on mobile devices using the offloading techniques. A literature survey about MCC, has shown that there is no support heterogeneity on reported studies. In response, we propose a framework called MpOS (Multi-platform Offloading System), which supports the offloading technique in mobile application development, for two mobile platforms (Android and Windows Phone). Two case studies were developed with MpOS solution in order to evaluate the framework for each mobile platform. These case studies show how the offloading technique works on several perspectives. In BenchImage experiment, the offloading performance was analyzed, concerning to its execution on a remote execution site (a cloudlet on local network and public cloud in the Internet). The Collision application promotes the analysis of the offloading technique performance on real-time application, also using different serialization systems. In both experiments, results show some situations where it was better to run locally on smarphone, than performing the offloading operation and vice versa.

Information technology and social media have been a driving force in the economy and have transformed all aspects of business in recent decades. Understanding social networks is necessary to evaluate their impacts and examine key business issues involving information and technological innovations. The dissertation contains three chapters exploring those issues. In the first chapter, I propose an optimal procurement mechanism for mobile data offloading, covering both technological and business aspects. The unprecedented growth of cellular traffic driven by web surfing, video streaming, and cloud-based services is creating challenges for cellular service providers to fulfill the unmet demand. My present work contributes to the existing literature by developing an analytical model, which considers the unique challenge of integrating the longer range cellular resource and shorter range WiFi hotspots. In the second chapter, I examine the effect of a social network on prediction markets using a controlled laboratory experiment. In prediction markets, people place bets on events that they think are most likely to happen, thus revealing in a sense the nature of their private information. Through a randomized experiment, I show that when the cost of information acquisition is low, a social-network-embedded prediction market outperforms a non-networked prediction market. The third chapter studies different forms of social learning in the context of location-based networks: observational learning and the saliency effect. In recent years, the location-sensing mobile devices offer geographic location capabilities to share users' information about their locations with their friends. In our context, observational learning corresponds to the fact that "check-ins" made by friends help users learn the quality information of a venue; the saliency effect refers to that check-ins lead some of the uninformed consumers to discover a new venue. / text

Mobile devices are increasingly being relied on for computation intensive and/or communication intensive applications that go beyond simple connectivity and demand more complex processing. This has been made possible by two trends. First, mobile devices, such as smartphones and tablets, are increasingly capable devices with processing and storage capabilities that make significant step improvements with every generation. Second, many improved connectivity options (e.g., 3G, WiFi, Bluetooth) are also available to mobile devices. In the rich computing and communication environment, it is promising but also challenging for mobile devices to take advantage of various available resources to improve the performance of mobile applications. First, with varying connectivity, remote computing resources are not always accessible to mobile devices in a predictable way. Second, given the uncertainty of connectivity and computing resources, their contention will become severe. This thesis seeks to address the connectivity challenges for mobile computing and communication. We propose a set of techniques and systems that help mobile applications to better handle the varying network connectivity in the utilization of various computation and communication resources. This thesis makes the following contributions: We design and implement Serendipity to allow a mobile device to use other encountered, albeit intermittently, mobile devices to speedup the execution of parallel applications through carefully allocating computation tasks among intermittently connected mobile devices. We design and implement IC-Cloud to enable a group of mobile devices to efficiently use the cloud computing resources for computation offloading even when the connectivity is varying or intermittent. We design and implement COSMOS to provide scalable computation offloading service to mobile devices at low cost by efficiently managing and allocating cloud computing resources. We design and implement CoAST to allow collaborative application-aware scheduling of mobile traffic to reduce the contention for bandwidth among communication-intensive applications without affecting their user experience.

As the demands of computational science and engineering simulations increase, the size and capabilities of High Performance Computing (HPC) clusters are also expected to grow. Consequently, the software providing the application programming abstractions for the clusters must adapt to meet these demands. Specifically, the increased cost of interprocessor synchronization and communication in larger systems must be accommodated. Non-blocking operations that allow communication latency to be hidden by overlapping it with computation have been proposed to mitigate this problem. In this work, we investigate offloading a portion of the communication processing to dedicated hardware in order to support communication/computation overlap efficiently. We work with the Message Passing Interface (MPI), the de facto standard for parallel programming in HPC environments. We investigate both point-to-point non-blocking communication and collective operations; our work with collectives focuses on the allgather operation. We develop designs for both flat and hierarchical cluster topologies and examine both eager and rendezvous communication protocols. We also develop a generalized primitive operation with the aim of simplifying further research into non-blocking collectives. We propose a new algorithm for the non-blocking allgather collective and implement it using this primitive. The algorithm has constant resource usage even when executing multiple operations simultaneously. We implemented these designs using CORE-Direct offloading support in Mellanox InfiniBand adapters. We present an evaluation of the designs using microbenchmarks and an application kernel that shows that offloaded non-blocking communication operations can provide latency that is comparable to that of their blocking counterparts while allowing most of the duration of the communication to be overlapped with computation and remaining resilient to process arrival and scheduling variations. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2014-05-29 11:55:53.87

Computational offloading advances the deployment of Mobile Edge Computing (MEC) in the next generation communication networks. However, the distributed nature of the mobile users and the complex applications make it challenging to schedule the tasks reasonably among multiple devices. Therefore, by leveraging the idea of Software-Defined Networking (SDN) and Service Composition (SC), we propose a Software-Defined Service Composition model (SDSC). In this model, the SDSC controller is deployed at the edge of the network and composes service in a centralized manner to reduce the latency of the task execution and the traffic on the access links by satisfying the user-specific requirement. We formulate the low latency service composition as a Constraint Satisfaction Problem (CSP) to make it a user-centric approach. With the advent of the SDN, the global view and the control of the entire network are made available to the network controller which is further leveraged by our SDSC approach. Furthermore, the service discovery and the offloading of tasks are designed for MEC environment so that the users can have a complex and robust system. Moreover, this approach performs the task execution in a distributed manner. We also define the QoS model which provides the composition rule that forms the best possible service composition at the time of need. Moreover, we have extended our SDSC model to involve the constant mobility of the mobile devices. To solve the mobility issue, we propose a mobility model and a mobility-aware QoS approach enabled in the SDSC model. The experimental simulation results demonstrate that our approach can obtain better performance than the energy saving greedy algorithm and the random offloading approach in a mobile environment.

With an ever-increasing percentage of the human population connected to the internet, the amount of data produced and processed is at an all-time high. Edge Computing has emerged as a paradigm to handle this growth and, combined with 5G, enables complex time-sensitive applications running on resource-restricted devices. This master thesis investigates the use of WebAssembly in the context of computa¬tional offloading at the Edge. The focus is on utilizing WebAssembly to move computa¬tional heavy parts of a system from an end device to an Edge Server. An objective is to improve program performance by reducing the execution time and energy consumption on the end device. A proof-of-concept offloading system is developed to research this. The system is evaluated on three different use cases; calculating Fibonacci numbers, matrix multipli¬cation, and image recognition. Each use case is tested on a Raspberry Pi 3 and Pi 4 comparing execution of the WebAssembly module both locally and offloaded. Each test will also run natively on both the server and the end device to provide some baseline for comparison.

Offloading tasks to a network processor is one of the important ways to increase server performance. Hardware offloading of Transmission Control Protocol/Internet Protocol (TCP/IP) intensive tasks is known to significantly improve performance. When the entire application is considered for offloading, the impact on the server can be significant because it significantly reduces the load on the server. The goal of this thesis is to consider such a system with application-level offloading, rather than hardware offloading, and gauge its performance benefits. I am implementing this project on an Apache httpd server (running RedHat Linux), on a system that utilizes a co-located network processor system (IXP2855). The performance of the two implementations is measured using the SPECweb2005 benchmark, which is the accepted industry standard for evaluating Web server performance.

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.

In this thesis we consider a set of mobile users that employ cloud-based computation offloading. In computation offloading, user energy consumption can be decreased by uploading and executing jobs on a remote server, rather than processing the jobs locally. In order to execute jobs in the cloud however, the user uploads must occur over a base station channel which is shared by all of the uploading users. Since the job completion times are subject to hard deadline constraints, this restricts the feasible set of jobs that can be remotely processed, and may constrain the users ability to reduce energy usage. The system is modelled as a competitive game in which each user is interested in minimizing its own energy consumption. The game is subject to the real-time constraints imposed by the job execution deadlines, user specific channel bit rates, and the competition over the shared communication channel. The thesis shows that for a variety of parameters, a game where each user independently sets its offloading decisions always has a pure Nash equilibrium, and a Gauss-Seidel method for determining this equilibrium is introduced. Results are presented which illustrate that the system always converges to a Nash equilibrium using the Gauss-Seidel method. Data is also presented which show the number of Nash equilibria that are found, the number of iterations required, and the quality of the solutions. We find that the solutions perform well compared to a lower bound on total energy performance. / Thesis / Master of Applied Science (MASc)