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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Task Offloading and Resource Allocation Using Deep Reinforcement Learning

Zhang, Kaiyi 01 December 2020 (has links)
Rapid urbanization poses huge challenges to people's daily lives, such as traffic congestion, environmental pollution, and public safety. Mobile Internet of things (MIoT) applications serving smart cities bring the promise of innovative and enhanced public services such as air pollution monitoring, enhanced road safety and city resources metering and management. These applications rely on a number of energy constrained MIoT units (MUs) (e.g., robots and drones) to continuously sense, capture and process data and images from their environments to produce immediate adaptive actions (e.g., triggering alarms, controlling machinery and communicating with citizens). In this thesis, we consider a scenario where a battery constrained MU executes a number of time-sensitive data processing tasks whose arrival times and sizes are stochastic in nature. These tasks can be executed locally on the device, offloaded to one of the nearby edge servers or to a cloud data center within a mobile edge computing (MEC) infrastructure. We first formulate the problem of making optimal offloading decisions that minimize the cost of current and future tasks as a constrained Markov decision process (CMDP) that accounts for the constraints of the MU battery and the limited reserved resources on the MEC infrastructure by the application providers. Then, we relax the CMDP problem into regular Markov decision process (MDP) using Lagrangian primal-dual optimization. We then develop advantage actor-critic (A2C) algorithm, one of the model-free deep reinforcement learning (DRL) method to train the MU to solve the relaxed problem. The training of the MU can be carried-out once to learn optimal offloading policies that are repeatedly employed as long as there are no large changes in the MU environment. Simulation results are presented to show that the proposed algorithm can achieve performance improvement over offloading decisions schemes that aim at optimizing instantaneous costs.
2

Návrhová studie realizace cloudletů pomocí komponentového modelu DEECo / DEECo Cloudlets Exploratory Study

Kinšt, Jakub January 2015 (has links)
This thesis explores possibilities of using recently introduced ensemble-based component models (represented by the DEECo model) as a management layer in a cloudlet-like environment for mobile computation offloading, which can lead to savings of limited mobile-specific resources such as battery life. As a part of the solution, the goal is analyzed, possible issues and problems are identified and then addressed by designing and implementing a reference architecture for computation offloading of parts of mobile applications managed by a DEECo-based control layer. The implementation is presented in the form of an offloading framework, whose use is demonstrated on two applications for the Android platform. Finally, the framework's performance and utility is evaluated by comparing offloaded and local executions of the same application. Powered by TCPDF (www.tcpdf.org)
3

Optimal Mobile Computation Offloading With Hard Task Deadlines

Hekmati, Arvin January 2019 (has links)
This thesis considers mobile computation offloading where task completion times are subject to hard deadline constraints. Hard deadlines are difficult to meet in conventional computation offloading due to the stochastic nature of the wireless channels involved. Rather than using binary offload decisions, we permit concurrent remote and local job execution when it is needed to ensure task completion deadlines. The thesis addresses this problem for homogeneous Markovian wireless channels. Two online energy-optimal computation offloading algorithms, OnOpt and MultiOpt, are proposed. OnOpt uploads the job to the server continuously and MultiOpt uploads the job in separate parts, each of which requires a separate offload initiation decision. The energy optimality of the algorithms is shown by constructing a time-dilated absorbing Markov process and applying dynamic programming. Closed form results are derived for general Markovian channels. The Gilbert-Elliott channel model is used to show how a particular Markov chain structure can be exploited to compute optimal offload initiation times more efficiently. The performance of the proposed algorithms is compared to three others, namely, Immediate Offloading, Channel Threshold, and Local Execution. Performance results show that the proposed algorithms can significantly improve mobile device energy consumption compared to the other approaches while guaranteeing hard task execution deadlines. / Thesis / Master of Applied Science (MASc)
4

IoTA: Internet of Things Assistant

Okumura, Brandon M 01 July 2017 (has links)
The Internet of Things is the networking of electronic devices, or “Things”, that enables them to collect and share data, as well as interact with their physical surround- ings. Analyzing this collected data allows us to make smarter economic decisions. These interconnected networks are usually driven by low-powered micro-controllers or cheap CPUs that are designed to function optimally with very little hardware. As scale and computational requirements increase, these micro-controllers are unable to grow without being physically replaced. This thesis proposes a system, IoTA, that assists the Internet of Things by pro- viding a shared computational resource for endpoint devices. This solution extends the functionality of endpoint devices without the need of physical replacement. The IoTA system is designed to be easily integrable to any existing IoT network. This system presents a model that allows for seamless processing of jobs submitted by endpoint devices while keeping scalability and flexibility in mind. Additionally, IoTA is built on top of existing IoT protocols. Evaluation shows there is a significant performance benefit in processing computationally heavy algorithms on the IoTA system as compared to processing them locally on the endpoint devices themselves.
5

Efficient Mobile Computation Offloading with Hard Task Deadlines and Concurrent Local Execution

Teymoori, Peyvand January 2021 (has links)
Mobile computation offloading (MCO) can alleviate the hardware limitations of mobile devices by migrating heavy computational tasks from mobile devices to more powerful cloud servers. This can lead to better performance and energy savings for the mobile devices. This thesis considers MCO over stochastic wireless channels when task completion times are subject to hard deadline constraints. Hard deadlines, however, are difficult to meet in conventional computation offloading due to the randomness caused by the wireless channels. In the proposed offloading policies, concurrent local execution (CLE) is used to guarantee task execution time constraints. By sometimes allowing simultaneous local and remote execution, CLE ensures that job deadlines are always satisfied in the face of any unexpected wireless channel conditions. The thesis introduces online optimal algorithms that reduce the remote and local execution overlap so that energy wastage is minimized. Markov processes are used to model the communication channels. MCO is addressed for three different job offloading schemes: continuous, multi-part, and preemptive. In the case of continuous offloading, referred to as 1-Part offloading, the mobile device will upload the entire job in one piece without interruption, when the scheduler decides to do so. In multi-part computation offloading, the job is partitioned into a known number (K) of parts, and each part is uploaded separately. In this offloading mechanism, which is referred to as K-Part Offloading, the upload initiation times of each part must be determined dynamically during runtime, and there may be waiting time periods between consecutive upload parts. Preemptive offloading is a generalization of K-Part Offloading where the number of task upload parts is unknown. In this scheme, a decision to either continue offloading or to temporarily interrupt the offload is made at the start of each time slot. Compared to the conventional contiguous computation offloading, interrupted offloading mechanisms (i.e., K-Part and preemptive offloading) allow the system to adapt when channel conditions change and therefore may result in lower mobile device energy consumption. This energy reduction will be obtained at the expense of having higher computational complexity. In this thesis, for each offloading scheme, an online computation offloading algorithm is introduced by constructing a time-dilated absorbing Markov chain (TDAMC) and applying dynamic programming (DP). These algorithms are shown to be energy-optimal while ensuring that the hard task deadline constraints are always satisfied. The optimality of these algorithms is proved using Markovian decision process stopping theory. Since the computational complexity of the proposed online algorithms, especially in the case of preemptive offloading, can be significant, three simpler and computationally efficient approximation methods are introduced: Markovian Compression (MC), Time Compression (TC), and Preemption Using Continuous Offloading (Preemption-CO). MC and TC reduce the state space of the offloading Markovian process by using a novel notion of geometric similarity or by running an optimal online offloading algorithm in periodic time steps. In Preemption-CO, while a task is offloaded preemptively, the offloading decision at every time-slot is based on non-preemptive calculations. These methods are used alone or in combination to construct practical offloading algorithms. A variety of results are presented that show the tradeoffs between complexity and mobile energy-saving performance for the different algorithms. / Thesis / Doctor of Philosophy (PhD)
6

Consumo de energia em dispositivos móveis Android: análise das estratégias de comunicação utilizadas em Computation Offloading / Energy consumption on Android mobile devices: communication strategies analysis used in Computation Offloading

Carolina Luiza Chamas 14 December 2017 (has links)
Os dispositivos móveis passaram por grandes transformações na última década e tornaram-se complexos computadores dotados de grande poder de processamento e memória, além de prover aos usuários diversos recursos como sensores e câmeras de alta resolução. O uso de dispositivos móveis para diversas tarefas aumentou consideravelmente, o que levantou uma grande preocupação com o o alto consumo de energia desses dispositivos. Portanto, estudos tem sido realizados no sentido de encontrar soluções para diminuir o custo de energia das aplicações que executam em dispositivos móveis. Uma das alternativas mais utilizadas é o \\textit{computation offloading}, cujo objetivo é transferir a execução de uma tarefa para uma plataforma externa com o intuito de aumentar desempenho e reduzir consumo de recursos, como a bateria, por exemplo. Decidir sobre usar ou não esta técnica implica entender a influência de fatores como a quantidade de dados processados, a quantidade de computação envolvida, e o perfil da rede. Muitos estudos tem sido realizados para estudar a influência de diversas opções de rede wireless, como 3G, 4G e Wifi, mas nenhum estudo investigou a influência das escolhas de comunicação no custo de energia. Portanto, o objetivo deste trabalho é apresentar uma investigação sobre a influência da quantidade de dados, da quantidade de computação e dos protocolos de comunicação ou estilo arquitetural no consumo de energia quando a técnica de \\textit{computation offloading} é utilizada. Neste estudo, foram comparados REST, SOAP, Socket e RPC na execução de algoritmos de ordenação de diferentes complexidades aplicados sobre vetores de diversos tamanhos e tipos de dados. Os resultados mostram que a execução local é mais econômica com algoritmos menos complexos, pequeno tamanho de entrada e tipo de dados menos complexos. Quando se trata de execução remota, o REST é a escolha mais econômica seguida por Socket. Em geral, REST é mais econômico com vetores do tipo Object, independentemente da complexidade do algoritmo e tamanho do vetor, enquanto Socket é mais econômico com entradas maiores e com vetores de tipos primitivos, como Int e Float / Mobile devices have significantly changed in the last decade and they become complex computer machines equipped with large processing power and memory. Moreover, they provide users with several resources such as sensors and high resolution cameras. The usage of mobile devices has significantly increased in the past years, which raised an important concern regarding the high energy consumption. Therefore, several investigations have been conducted aiming at finding solutions to reduce the energy cost of mobile applications. One of the most used strategy is called computation offloading, whose main goal is to transfer the execution of a task to an external platform aiming at increasing performance and reducing resource consumption, including the battery. Deciding towards offloading certain tasks requires to understand the influence of the amount of data, amount of computation, and the network profile. Several studies have investigated the influence of different wireless flavours, such as 3G, 4G and wifi, but no study has investigated the influence of the communication choices on the energy cost. Therefore, the purpose of this research project is to present an investigation on the influence of the amount of data, amount of computation and the communication protocols and architectural style on the energy consumption in the context of the computation offloading technique. In this study, we compare REST, SOAP, Socket and RPC when executing algorithms of different complexities and different input sizes and types. Results show that local execution is more economic with less complex algorithms and small input data. When it comes to remote execution, REST is the most economic choice followed by Socket. In general, REST is the most economic choice when applied on Object type arrays, regardless the complexity and size, while Socket is the most economic choice with large arrays and primitive types such as integers and floats
7

Análise de risco em operações de \'offloading\' - um modelo de avaliação probabilística dinâmica para a tomada de decisão. / Risk analysis of offloading operations - probabilistic evaluation model for dynamic decision making.

Carmen Elena Patiño Rodriguez 16 February 2012 (has links)
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.
8

An approach for Mobile Multiplatform Offloading System / Uma abordagem para Offloading em MÃltiplas Plataformas MÃveis

Philipp Bernardino Costa 25 August 2014 (has links)
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.
9

Análise de risco em operações de \'offloading\' - um modelo de avaliação probabilística dinâmica para a tomada de decisão. / Risk analysis of offloading operations - probabilistic evaluation model for dynamic decision making.

Patiño Rodriguez, Carmen Elena 16 February 2012 (has links)
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.
10

Addressing connectivity challenges for mobile computing and communication

Shi, Cong 27 August 2014 (has links)
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.

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