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.

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This study investigates the feasibility and performance of SLAM (Simultaneous Localization and Mapping) as a service (SLAM-as-a-Service) for outdoor augmented reality (AR) applications. Given the rapid advancements in AR technology, integrating lightweight AR glasses with real-time SLAM capabilities poses significant challenges, particularly due to the computational demands of SLAM algorithms and the limited hardware capacity of AR devices. This study proposes a scalable SLAM-as-a-Service framework that offloads intensive computational tasks to remote servers, leveraging cloud and edge computing resources. The ORB-SLAM3 algorithm, known for its robustness and real-time processing capabilities, was adapted and implemented in a service-oriented architecture. The framework was evaluated using the EuRoC dataset to benchmark processing speed, accuracy, and round trip time. The results indicate that while the proposed SLAM-as-a-Service model shows promise in handling high computational loads, several obstacles need to be addressed to achieve minimal round trip time and ensure a seamless AR experience. This thesis contributes to the development of scalable and efficient AR solutions by addressing the limitations of on device processing and highlighting the potential of cloud-based services in enhancing the performance and feasibility of AR applications in dynamic outdoor environments.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-04-25T12:03:54Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_AdrianoHenrique.pdf: 1956295 bytes, checksum: 38ce5d73db0d44416c8653e58120f11c (MD5) / Made available in DSpace on 2017-04-25T12:03:55Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_AdrianoHenrique.pdf: 1956295 bytes, checksum: 38ce5d73db0d44416c8653e58120f11c (MD5) Previous issue date: 2016-08-29 / Apesar da grande evolução tecnológica nos hardwares dos dispositivos móveis e nas redes sem fio, ainda existem grandes limitações nesses dispositivos em termos de processamento, capacidade de armazenamento e autonomia de energia, quando comparados aos desktops e servidores. O paradigma de Computação em Nuvem Móvel (MCC – Mobile Cloud Computing) permite estender os recursos computacionais dos dispositivos móveis através da utilização das técnicas de offloading computacional possibilitando um melhor desempenho as aplicações e uma redução no consumo das baterias dos dispositivos. Entretanto, a técnica de offloading nem sempre traz benefícios para o dispositivo móvel em situações de constante mobilidade do usuário, já que cada mudança de rede requer que o processo de offloading seja refeito. Esta dissertação propõe uma solução para otimizar o consumo de energia e o tempo de resposta durante as operações de offloading computacional quando o dispositivo muda de ponto de acesso. A proposta considera um esquema de gerenciamento de mobilidade baseado em Software Defined Networking (SDN) e técnica de caching remoto, que permite ao usuário receber o resultado do offloading no próximo acesso à rede, mesmo que esse fique desconectado por um longo período. A solução foi implementada em um testbed WiFi, com acesso ao ambiente MCC utilizando cloudlet baseada na plataforma OpenStack e integrada ao controlador SDN OpenDaylight. O consumo de energia obtido pela proposta que utiliza SDN/OpenFlow para o gerenciamento de mobilidade chegou a ser 11,33 vezes menor e a velocidade de processamento foi 3,23 vezes maior que do ambiente tradicional. O sistema de caching remoto, apesar de se mostrar útil em relação à rápida entrega dos resultados já processados, elevou consideravelmente o consumo de energia da bateria. A técnica de caching remoto é indicada para os casos nos quais a aplicação envia à cloudlet um grande volume de dados para ser processado e o nível da bateria do dispositivo encontra-se em estado não crítico ou quando o usuário enfrenta um longo período sem comunicação com a cloudlet. / Although the great technological evolution in the mobile devices hardware and wireless networks, remains significant limitations of these devices regarding processing, storage, and energy, when compared to desktops and servers. The paradigm of Mobile Cloud Computing (MCC) allows to extend the computational resources of the mobile devices through the use of computational offloading techniques, achieving a better performance on the part of the applications and a reduction in the battery consumption of the devices. The offloading technique does not always bring benefits to a mobile device in situations of high mobility since each network change requires the execution of the offloading process. This dissertation proposes a solution to optimize energy consumption and response times during the computational offloading operations when the device change of access points (AP). To this end, the proposal considers for such, a mobility management scheme based on SDN (Software Defined Networking) and a remote caching technique, that allows the user to receive the result from offloading in the next AP, even if he stays disconnected for an extended period. The solution was implemented in one Wi-Fi testbed, with access to the MCC environment using cloudlet based on the OpenStack platform and integrated with the OpenDaylight SDN controller. The achieved reduction of energy consumption for the mobility management proposal arrived to be 11.33 times lower, and the processing speed was 3.23 times bigger that of the traditional environment. The remote caching system, although useful in fast delivering the already processed results, considerably raised the battery energy consumption. Thus, the applicability of remote caching limits it to the cases where the application sends to the cloudlet an enormous volume of data to be processed and the battery level of the device is not critical or when the user faces an extended period without communication with the cloudlet.