LIMA FILHO, J. S. Um Mecanismo de offloading de dados com tomada de decisão. 2017. 63 f. Dissertação (Mestrado em Engenharia de Teleinformática)–Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2017. / Submitted by Renato Vasconcelos (ppgeti@ufc.br) on 2017-09-08T18:15:22Z No. of bitstreams: 1 2017_dis_jslimafilho.pdf: 1815706 bytes, checksum: 4259b6681ae6971d1aa77d5dd975ec5b (MD5) / Approved for entry into archive by Marlene Sousa (mmarlene@ufc.br) on 2017-09-08T19:04:32Z (GMT) No. of bitstreams: 1 2017_dis_jslimafilho.pdf: 1815706 bytes, checksum: 4259b6681ae6971d1aa77d5dd975ec5b (MD5) / Made available in DSpace on 2017-09-08T19:04:33Z (GMT). No. of bitstreams: 1 2017_dis_jslimafilho.pdf: 1815706 bytes, checksum: 4259b6681ae6971d1aa77d5dd975ec5b (MD5) Previous issue date: 2017-07-28 / According to IBGE (Brazilian Institute of Geography and Statistics), 92.1% of home access to the Internet was made by mobile phones. Due to the physical limitations, the processing power of these devices and the life of the batteries have not matched the growing demand of mobile applications. In the mobile cloud computing paradigm, offloading techniques are used to augment computation and power capacities of mobile devices as well as to reduce the execution time of tasks. In this dissertation, we propose a data offloading mechanism that selects and migrates files to a local infrastructure (cloudlet), assisting computation offloading frameworks to reduce the amount of data sent over the network. The mechanism uses the application methods execution history, as well as the network condition, to create decision trees that help deciding when and which files used by these methods should be transferred. The experiments results indicate that our mechanism reduces the processing offloading time by up to 19.5%. / De acordo com o IBGE (Instituto Brasileiro de Geografia e Estatística), 92,1% do acesso domici- liar à Internet passou a ser feito pelos telefones móveis celulares. Devido às limitações físicas, o poder de processamento desses dispositivos e o tempo de vida das baterias não têm acompanhado a exigência crescente dos aplicativos móveis. No paradigma de mobile cloud computing, as técnicas de offloading permitem a extensão das capacidades energética e computacional de dispositivos móveis, bem como a redução do tempo de execução de procedimentos. Nesta dis- sertação, propomos um mecanismo de offloading de dados que seleciona e migra arquivos para uma infraestrutura local (cloudlet) auxiliando os frameworks de offloading de processamento a reduzirem a quantidade de dados enviados pela rede. O mecanismo utiliza-se do histórico de execuções dos métodos dos aplicativos, assim como das condições da rede, para criar árvores de decisão que auxiliam na decisão de quando e quais arquivos utilizados por estes métodos devem ser transferidos. Os resultados dos experimentos indicam que a utilização do mecanismo proposto reduz o tempo do offloading de processamento em até 19,5%.

In mobile cloud computing (MCC), rich mobile application data is processed at the cloud infrastructure by reliving resource limited mobile devices from computationally complex tasks. However, due to the ubiquitous and mobility nature, providing time critical rich applications over remote cloud infrastructure is a challenging task for mobile application service providers. Therefore, according to the literature, close proximity placement of cloud services has been identified as a way to achieve lower end-to-end access delay and thereby provide a higher quality of experience (QoE) for rich mobile application users. However, providing a higher Quality of Service (QoS) with mobility is still a challenge within close proximity clouds. Access delay to a closely placed cloud tends to be increased over time when users move away from the cloud. However, reactive resource relocation mechanism proposed in literature does not provide a comprehensive mechanism to guarantee the QoS and as well as to minimize service provisioning cost for mobile cloud service providers. As a result, using the benefits of SDN and the data plane programmability with logically centralized controllers, a resource allocation framework was proposed for IaaS mobile clouds with regional datacenters. The user mobility problem was analyzed within SDN-enabled wireless networks and addressed the possible service level agreement violations that could occur with inter-regional mobility. The proposed framework is composed of an optimization algorithm to provide seamless cloud service during user mobility. Further a service provisioning cost minimization criteria was considered during an event of resource allocation and inter-regional user mobility.

Over a period of time, it has been studied that a mobile “edge-cloud” formed by hand-held devices could be a productive resource entity for providing a service in the mobile cloud landscape. The ease of access to a pool of devices is much more arbitrary and based purely on the needs of the user. This pool can act as a provider of an infrastructure for various services that can be processed with volunteer node participation, where the node in the vicinity is itself a service provider. This representation of cloud formation to engender a constellation of devices in turn providing a service is the basis for the concept of Mobile Ad-hoc Cloud Computing. In this thesis, an architecture is designed for providing an Infrastructure as a service in Mobile Ad-hoc Cloud Computing. The performance evaluation reveals the gain in execution time while offloading to the mobile ad-hoc cloud. Further, this novel architecture enables discovering a dedicated pool of volunteer devices for computation. An optimized task scheduling algorithm is proposed that provides a coordinated resource allocation. However, failure to maintain the service between heterogeneous networks shows the inability of the present day networks to adapt to frequent changes in a network. Thus, owing to the heavy dependence on the centralized mobile network, the service related issues in a mobile ad-hoc cloud needs to be addressed. As a result, using the principles of Software Defined Networking (SDN), a disruption tolerant Mobile Ad-hoc Cloud framework is proposed. To evaluate this framework a comprehensive case study is provided in this work that shows a round trip time improvement using an SDN controller.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19132号 / 情博第578号 / 新制||情||101(附属図書館) / 32083 / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 高橋 達郎, 教授 原田 博司, 教授 梅野 健 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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Yes / Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data. / Supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904).

A future Internet of Things (IoT) system will consist of a huge quantity of heterogeneous IoT devices, each capable of providing services upon request. It is of utmost importance for an IoT device to know if another IoT service is trustworthy when requesting it to provide a service. In this dissertation research, we develop trust-based service management techniques applicable to distributed, centralized, and hybrid IoT environments. For distributed IoT systems, we develop a trust protocol called Adaptive IoT Trust. The novelty lies in the use of distributed collaborating filtering to select trust feedback from owners of IoT nodes sharing similar social interests. We develop a novel adaptive filtering technique to adjust trust protocol parameters dynamically to minimize trust estimation bias and maximize application performance. Our adaptive IoT trust protocol is scalable to large IoT systems in terms of storage and computational costs. We perform a comparative analysis of our adaptive IoT trust protocol against contemporary IoT trust protocols to demonstrate the effectiveness of our adaptive IoT trust protocol. For centralized or hybrid cloud-based IoT systems, we propose the notion of Trust as a Service (TaaS), allowing an IoT device to query the service trustworthiness of another IoT device and also report its service experiences to the cloud. TaaS preserves the notion that trust is subjective despite the fact that trust computation is performed by the cloud. We use social similarity for filtering recommendations and dynamic weighted sum to combine self-observations and recommendations to minimize trust bias and convergence time against opportunistic service and false recommendation attacks. For large-scale IoT cloud systems, we develop a scalable trust management protocol called IoT-TaaS to realize TaaS. For hybrid IoT systems, we develop a new 3-layer hierarchical cloud structure for integrated mobility, service, and trust management. This architecture supports scalability, reconfigurability, fault tolerance, and resiliency against cloud node failure and network disconnection. We develop a trust protocol called IoT-HiTrust leveraging this 3-layer hierarchical structure to realize TaaS. We validate our trust-based IoT service management techniques developed with real-world IoT applications, including smart city air pollution detection, augmented map travel assistance, and travel planning, and demonstrate that our trust-based IoT service management techniques outperform contemporary non-trusted and trust-based IoT service management solutions. / Ph. D.

abstract: Cloud computing is regarded as one of the most revolutionary technologies in the past decades. It provides scalable, flexible and secure resource provisioning services, which is also the reason why users prefer to migrate their locally processing workloads onto remote clouds. Besides commercial cloud system (i.e., Amazon EC2), ProtoGENI and PlanetLab have further improved the current Internet-based resource provisioning system by allowing end users to construct a virtual networking environment. By archiving the similar goal but with more flexible and efficient performance, I present the design and implementation of MobiCloud that is a geo-distributed mobile cloud computing platform, and G-PLaNE that focuses on how to construct the virtual networking environment upon the self-designed resource provisioning system consisting of multiple geo-distributed clusters. Furthermore, I conduct a comprehensive study to layout existing Mobile Cloud Computing (MCC) service models and corresponding representative related work. A new user-centric mobile cloud computing service model is proposed to advance the existing mobile cloud computing research. After building the MobiCloud, G-PLaNE and studying the MCC model, I have been using Software Defined Networking (SDN) approaches to enhance the system security in the cloud virtual networking environment. I present an OpenFlow based IPS solution called SDNIPS that includes a new IPS architecture based on Open vSwitch (OVS) in the cloud software-based networking environment. It is enabled with elasticity service provisioning and Network Reconfiguration (NR) features based on POX controller. Finally, SDNIPS demonstrates the feasibility and shows more efficiency than traditional approaches through a thorough evaluation. At last, I propose an OpenFlow-based defensive module composition framework called CloudArmour that is able to perform query, aggregation, analysis, and control function over distributed OpenFlow-enabled devices. I propose several modules and use the DDoS attack as an example to illustrate how to composite the comprehensive defensive solution based on CloudArmour framework. I introduce total 20 Python-based CloudArmour APIs. Finally, evaluation results prove the feasibility and efficiency of CloudArmour framework. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014

Today, the increasing number of applications based on the Internet of Things, as well as advances in wireless communication, information and communication technology, and mobile cloud computing have allowed users to access a wide range of resources while mobile. Vehicular clouds are considered key elements for today’s intelligent transportation systems. They are outfitted with equipment to enable applications and services for vehicle drivers, surrounding vehicles, pedestrians and third parties. As vehicular cloud computing has become more popular, due to its ability to improve driver and vehicle safety and provide provisioning services and applications, researchers and industry have growing interest in the design and development of vehicular networks for emerging applications. Though vehicle drivers can now access a variety of on-demand resources en route via vehicular network service providers, the development of vehicular cloud provisioning services has many challenges. In this dissertation, we examine the most critical provisioning service challenges drivers face, including, cost, privacy and latency. To this point, very little research has addressed these issues from the driver perspective. Privacy and service latency are certainly emerging challenges for drivers, as are service costs since this is a relatively new financial concept. Motivated by the Quality of Experience paradigm and the concept of the Trusted Third Party, we identify and investigate these challenges and examine the limitations and requirements of a vehicular environment. We found no research that addressed these challenges simultaneously, or investigated their effect on one another. We have developed a Quality of Experience framework that provides scalability and reduces congestion overhead for users. Furthermore, we propose two theory-based frameworks to manage on-demand service provision in vehicular clouds: Auction-driven Multi-objective Provisioning and a Multiagent/Multiobjective Interaction Game System. We present different approaches to these, and show through analytical and simulation results that our potential schemes help drivers minimize costs and latency, and maximize privacy.

The application offloading problem for Mobile Cloud Computing aims at improving the mobile user experience by leveraging the resources of the cloud. The execution of the mobile application is offloaded to the cloud, saving energy at the mobile device or speeding up the execution of the application. We improve the accuracy and performance of application offloading solutions in three main directions. First, we propose a novel fine-grained application model that supports complex module dependencies such as sequential, conditional and parallel module executions. The model also allows for multiple offloading decisions that are tailored towards the current application, network, or user contexts. As a result, the model is more precise in capturing the structure of the application and supports more complex offloading solutions. Second, we propose three cost models, namely, average-based, statistics-based and interval-based cost models, defined for the proposed application model. The average-based approach models each module cost by the expected cost value, and the expected cost of the entire application is estimated considering each of the three module dependencies. The novel statistics-based cost model employs Cumulative Distribution Function (CDFs) to represent the costs of the modules and of the mobile application, which is estimated considering the cost and dependencies of the modules. This cost model opens the doors for new statistics-based optimization functions and constraints whereas the state of the art only support optimizations based on the average running cost of the application. Furthermore, this cost model can be used to perform statistical analysis of the performance of the application in different scenarios such as varying network data rates. The last cost model, the interval-based, represents the module costs via intervals in order to addresses the cost uncertainty while having lower requirements and computational complexity than the statistics-based model. The cost of the application is estimated as an expected maximum cost via a linear optimization function. Finally, we present offloading decision algorithms for each cost model. For the average-based model, we present a fast optimal dynamic programming algorithm. For the statistics-based model, we present another fast optimal dynamic programming algorithm for the scenario where the optimization function meets specific properties. Finally, for the interval-based cost model, we present a robust formulation that solves a linear number of linear optimization problems. Our evaluations verify the accuracy of the models and show higher cost savings for our solutions when compared to the state of the art.