Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G

By caching content at network edges close to the users, the content-centric networking (CCN) has been considered to enforce efficient content retrieval and distribution in the fifth generation (5G) networks. Due to the volume, velocity, and variety of data generated by various 5G users, an urgent and strategic issue is how to elevate the cognitive ability of the CCN to realize context-awareness, timely response, and traffic offloading for 5G applications. In this article, we envision that the fundamental work of designing a cognitive CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to associatively learn and control the states of edge devices (such as phones, vehicles, and base stations) and in-network resources (computing, networking, and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework for C-CCN in 5G, which can aggregate the idle computing resources of the neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive learning tasks. By leveraging artificial intelligence (AI) to jointly processing sensed environmental data, dealing with the massive content statistics, and enforcing the mobility control at network edges, the FEL makes it possible for mobile users to cognitively share their data over the C-CCN in 5G. To validate the feasibility of proposed framework, we design two FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network acceleration, 2) enhanced mobility management. Simultaneously, we present the simulations to show the FEL's efficiency on serving for the mobile users' delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201

Traffic control for energy harvesting virtual small cells via reinforcement learning

Due to the rapid growth of mobile data traffic, future mobile networks are expected to support at least 1000 times more capacity than 4G systems. This trend leads to an increasing energy demand from mobile networks which raises both economic and environmental concerns. Energy costs are becoming an important part of OPEX by Mobile Network Operators (MNOs). As a result, the shift towards energy-oriented design and operation of 5G and beyond systems has been emphasized by academia, industries as well as standard bodies. In particular, Radio Access Network (RAN) is the major energy consuming part of cellular networks. To increase the RAN efficiency, Cloud Radio Access Network (CRAN) has been proposed to enable centralized cloud processing of baseband functions while Base Stations (BSs) are reduced to simple Radio Remote Heads (RRHs). The connection between the RRHs and central cloud is provided by high capacity and very low latency fronthaul. Flexible functional splits between local BS sites and a central cloud are then proposed to relax the CRAN fronthaul requirements via partial processing of baseband functions at the local BS sites. Moreover, Network Function Virtualization (NFV) and Software Defined Networking (SDN) enable flexibility in placement and control of network functions. Relying on SDN/NFV with flexible functional splits, network functions of small BSs can be virtualized and placed at different sites of the network. These small BSs are known as virtual Small Cells (vSCs). More recently, Multi-access Edge Computing (MEC) has been introduced where BSs can leverage cloud computing capabilities and offer computational resources on demand basis. On the other hand, Energy Harvesting (EH) is a promising technology ensuring both cost effectiveness and carbon footprint reduction. However, EH comes with challenges mainly due to intermittent and unreliable energy sources. In EH Base Stations (EHBSs), it is important to intelligently manage the harvested energy as well as to ensure energy storage provision. Consequently, MEC enabled EHBSs can open a new frontier in energy-aware processing and sharing of processing units according to flexible functional split options. The goal of this PhD thesis is to propose energy-aware control algorithms in EH powered vSCs for efficient utilization of harvested energy and lowering the grid energy consumption of RAN, which is the most power consuming part of the network. We leverage on virtualization and MEC technologies for dynamic provision of computational resources according to functional split options employed by the vSCs. After describing the state-of-the-art, the first part of the thesis focuses on offline optimization for efficient harvested energy utilization via dynamic functional split control in vSCs powered by EH. For this purpose, dynamic programming is applied to determine the performance bound and comparison is drawn against static configurations. The second part of the thesis focuses on online control methods where reinforcement learning based controllers are designed and evaluated. In particular, more focus is given towards the design of multi-agent reinforcement learning to overcome the limitations of centralized approaches due to complexity and scalability. Both tabular and deep reinforcement learning algorithms are tailored in a distributed architecture with emphasis on enabling coordination among the agents. Policy comparison among the online controllers and against the offline bound as well as energy and cost saving benefits are also analyzed.Debido al rápido crecimiento del tráfico de datos móviles, se espera que las redes móviles futuras admitan al menos 1000 veces más capacidad que los sistemas 4G. Esta tendencia lleva a una creciente demanda de energía de las redes móviles, lo que plantea preocupaciones económicas y ambientales. Los costos de energía se están convirtiendo en una parte importante de OPEX por parte de los operadores de redes móviles (MNO). Como resultado, la academia, las industrias y los organismos estándar han enfatizado el cambio hacia el diseño orientado a la energía y la operación de sistemas 5G y más allá de los sistemas. En particular, la red de acceso por radio (RAN) es la principal parte de las redes celulares que consume energía. Para aumentar la eficiencia de la RAN, se ha propuesto Cloud Radio Access Network (CRAN) para permitir el procesamiento centralizado en la nube de las funciones de banda base, mientras que las estaciones base (BS) se reducen a simples cabezales remotos de radio (RRH). La conexión entre los RRHs y la nube central es proporcionada por una capacidad frontal de muy alta latencia y muy baja latencia. Luego se proponen divisiones funcionales flexibles entre los sitios de BS locales y una nube central para relajar los requisitos de red de enlace CRAN a través del procesamiento parcial de las funciones de banda base en los sitios de BS locales. Además, la virtualización de funciones de red (NFV) y las redes definidas por software (SDN) permiten flexibilidad en la colocación y el control de las funciones de red. Confiando en SDN / NFV con divisiones funcionales flexibles, las funciones de red de pequeñas BS pueden virtualizarse y ubicarse en diferentes sitios de la red. Estas pequeñas BS se conocen como pequeñas celdas virtuales (vSC). Más recientemente, se introdujo la computación perimetral de acceso múltiple (MEC) donde los BS pueden aprovechar las capacidades de computación en la nube y ofrecer recursos computacionales según la demanda. Por otro lado, Energy Harvesting (EH) es una tecnología prometedora que garantiza tanto la rentabilidad como la reducción de la huella de carbono. Sin embargo, EH presenta desafíos principalmente debido a fuentes de energía intermitentes y poco confiables. En las estaciones base EH (EHBS), es importante administrar de manera inteligente la energía cosechada, así como garantizar el suministro de almacenamiento de energía. En consecuencia, los EHBS habilitados para MEC pueden abrir una nueva frontera en el procesamiento con conciencia energética y el intercambio de unidades de procesamiento de acuerdo con las opciones de división funcional flexible. El objetivo de esta tesis doctoral es proponer algoritmos de control conscientes de la energía en vSC alimentados por EH para la utilización eficiente de la energía cosechada y reducir el consumo de energía de la red de RAN, que es la parte más consumidora de la red. Aprovechamos las tecnologías de virtualización y MEC para la provisión dinámica de recursos computacionales de acuerdo con las opciones de división funcional empleadas por los vSC. La primera parte de la tesis se centra en la optimización fuera de línea para la utilización eficiente de la energía cosechada a través del control dinámico de división funcional en vSC con tecnología EH. Para este propósito, la programación dinámica se aplica para determinar el rendimiento limitado y la comparación se realiza con configuraciones estáticas. La segunda parte de la tesis se centra en los métodos de control en línea donde se diseñan y evalúan los controladores basados en el aprendizaje por refuerzo. En particular, se presta más atención al diseño de aprendizaje de refuerzo de múltiples agentes para superar las limitaciones de los enfoques centralizados debido a la complejidad y la escalabilidad. También se analiza la comparación de políticas entre los controladores en línea y contra los límites fuera de línea,Postprint (published version

A review on green caching strategies for next generation communication networks

© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching