User preference aware caching deployment for device-to-device caching networks

Content caching in the device-to-device (D2D) cellular networks can be utilized to improve the content delivery efficiency and reduce traffic load of cellular networks. In such cache-enabled D2D cellular networks, how to cache the diversity contents in the multiple cache-enabled mobile terminals, namely, the caching deployment, has a substantial impact on the network performance. In this paper, a user preference aware caching deployment algorithm is proposed for D2D caching networks. First, the definition of the user interest similarity is given based on the user preference. Then, a content cache utility of a mobile terminal is defined by taking the transmission coverage region of this mobile terminal and the user interest similarity of its adjacent mobile terminals into consideration. A general cache utility maximization problem with joint caching deployment and cache space allocation is formulated, where the special logarithmic utility function is integrated. In doing so, the caching deployment and the cache space allocation can be decoupled by equal cache space allocation. Subsequently, we relax the logarithmic utility maximization problem, and obtain a low complexity near-optimal solution via a dual decomposition method. Compared with the existing caching placement methods, the proposed algorithm can achieve significant improvement on cache hit ratio, content access delay, and traffic offloading gain

Energy-Efficient Context-Aware Matching for Resource Allocation in Ultra-Dense Small Cells

With the explosive growth of mobile data traffic and rapidly rising energy price, how to implement caching at small cells in an energy-efficient way is still an open problem and requires further research efforts. In this paper, we study the energy-efficient context-aware resource allocation problem, which falls into the category of mixed integer nonlinear programming (MINLP) and is NP-hard. To provide a tractable solution, the MINLP problem is decoupled and reformulated as a one-to-one matching problem under two-sided preferences, which are modeled as the maximum energy efficiency that can be achieved under the expected matching. An iterative algorithm is developed to establish preference profiles by employing nonlinear fractional programming and Lagrange dual decomposition. Then, we propose an energy-efficient matching algorithm based on the Gale-Shapley algorithm, and provide the detailed discussion and analysis of stability, optimality, implementation issues, and algorithmic complexity. The proposed matching algorithm is also extended to scenarios with preference, indifference, and incomplete preference lists by introducing some tie-breaking and preference deletion rules. The simulation results demonstrate that the proposed algorithm achieves significant performance and satisfaction gains compared with the conventional algorithms

Aplicação de modelos ocultos de Markov na distribuição de vídeo sob demanda

Orientador: Prof. Dr. Carlos Marcelo PedrosoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Eietrica. Defesa : Curitiba, 05/09/2018Inclui referências: p.54-56Área de concentração: TelecomunicaçõesResumo: O uso de aplicações de vídeo sob demanda já domina o trafego atual da Internet e continua com uma forte perspectiva de crescimento nos próximos anos. Existem algoritmos desenvolvidos com o objetivo de reduzir o consumo de banda e reduzir a carga de processamento do servidor durante a distribuição de vídeo. Estes algoritmos baseiam-se m majoritariamente na popularidade dos vídeos existentes na biblioteca do servidor. Algoritmos mais recentes exploram a capacidade de armazenamento do dispositivo do usuário para alocar segmentos iniciais dos vídeos, o que permite ao servidor a distribuição mais eficiente explorando melhor a capacidade de transmissão multicast da rede além de proporcionar ao usuário início imediato do vídeo requisitado. Nesta dissertação propõe-se um método de distribuição de vídeo capaz de prever a categoria do conteúdo que será acessado pelo usuário através de um Modelo Oculto de Markov (HMM, Hidden Markov Model), de modo a aumentar a eficiência na distribuição do vídeo. O desempenho do método foi avaliado através de um simulador baseado em eventos discretos desenvolvido em linguagem C. As analises comparam o impacto de diferentes taxas de requisições recebidas pelo servidor de vídeo, diferentes padrões de popularidade dos vídeos e para diferentes capacidades de armazenamento do dispositivo do usuário. Os resultados indicam que o uso do método pode diminuir significativamente o consumo de banda na rede IP durante a transmissão de vídeo quando comparado com métodos existentes. Com a aplicação do método proposto e possível atender um maior numero de requisições com o mesmo hardware, o que pode ser visto também como uma redução de custo de implementação para servidores VoD. Palavras-chave: Vídeo sob demanda. Distribuição de vídeo. Hidden Markov Models.Abstract: The use of video-on-demand applications have already overcome current Internet traffic and continues with a strong growth prospect in the coming years. There are algorithms designed to reduce bandwidth consumption and reduce the server processing load during video distribution. These algorithms are mostly based on the popularity of existing videos in the server library. More recent algorithms explore the storage capacity of the user's device to allocate initial segments of the videos, which allows the server to more efficiently distribute by exploiting the network's multicast transmission capability, in addition to providing the user with immediate video start-up requested. In this study we propose a video distribution method capable of predicting the category of content that will be accessed by the user through a Hidden Markov Model (h M m ) in order to increase efficiency in video distribution. The performance of the method was evaluated through a discrete event-based simulator developed in C language. The analyzes compare the impact of different rates of requests received by the video server, different patterns of video popularity and different storage capacities of the user's device. The results indicate that the use of the method can significantly reduce bandwidth consumption in the IP network during video transmission when compared to existing methods. With the application of the proposed method it is possible to meet a larger number of requests with the same hardware, which can also be seen as a reduction of implementation cost for VoD servers. Keyw ords: Video-on-demand. Video distribution. Hidden Markov Models