A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities

In the context of cloud-enabled 5G radio access networks with network function virtualization capabilities, we focus on the virtual network function placement problem for a multitenant cluster of small cells that provide mobile edge computing services. Under an emerging distributed network architecture and hardware infrastructure, we employ cloud-enabled small cells that integrate microservers for virtualization execution, equipped with additional hardware appliances. We develop an energy-aware placement solution using a robust optimization approach based on service demand uncertainty in order to minimize the power consumption in the system constrained by network service latency requirements and infrastructure terms. Then, we discuss the results of the proposed placement mechanism in 5G scenarios that combine several service flavours and robust protection values. Once the impact of the service flavour and robust protection on the global power consumption of the system is analyzed, numerical results indicate that our proposal succeeds in efficiently placing the virtual network functions that compose the network services in the available hardware infrastructure while fulfilling service constraints.The research leading to these results has been supported by the EU funded H2020 5G-PPP Project SESAME (Grant Agreement 671596) and the Spanish MINECO Project 5GRANVIR (TEC2016-80090-C2-2-R)

A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet

With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing

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