Campus realities: Forecasting user bandwidth utilization using monte carlo simulation

Adequate network design, planning, and improvement are pertinent in a campus network as the use of smart devices is escalating. Underinvesting and overinvesting in campus network devices lead to low network performance and low resource utilization respectively. Due to this fact, it becomes very necessary to ascertain if the current network capacity satisfies the available bandwidth requirement. The bandwidth demand varies from different times and periods as the number of connected devices is on the increase. Thus, emphasizing the need for adequate bandwidth forecast. This paper presents a Monte Carlo simulation model that forecast user bandwidth utilization in a campus network. This helps in planning campus network design and upgrade to deliver available content in a period of high and normal traffic load

Técnicas de previsão: aplicação a redes celulares

Due to the growing competitiveness and aggressiveness of the market, the network operators’ strategy is increasingly based on improving infrastructures and optimizing existing resources, in a way that provides the best experience to the user. To do this, the operator analyzes the Key Performance Indicators (KPIs) and uses forecasting methods to predict and plan the modifications needed in the network. With this as basis, this work focuses on the study and analysis of different forecasting methods and their implementation in Python, so that the operator can obtain automate real-time predictions of the future behavior of his network.Devido ao aumento da competitividade e agressividade do mercado, cada vez mais a estratégia dos operadores de redes móveis passa pelo melhoramento das infraestruturas e otimização dos recursos já existentes, de modo a proporcionar a melhor experiência aos seus utilizadores. Para isto, recorrem à análise de indicadores chave de desempenho (KPIs) e ao uso de métodos de previsão para prever e planear alterações a realizar na sua rede. Tendo isto como base, esta dissertação foca-se no estudo e análise de diferentes métodos de previsão e sua implementação em Python, de maneira a obter previsões do futuro comportamento da rede em tempo real e de forma automatizada.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Machine learning based heuristic BBU-RRH switching scheme for C-RAN in 5G

The immense increase in bandwidth demand by various services such as high definition video streaming, online gaming, and virtual reality has made it increasingly challenging for operators to provide satisfactory services to the end users while making a profit. Cloud Radio Access Network (C-RAN) is a new architecture that has been proposed to facilitate the mobile networks' ability to meet the increase in bandwidth demand. C-RAN consists of three parts, namely Remote Radio Head (RRH), the front haul link, and Baseband Processing Units (BBU) pool. Many RRHs are associated with one BBU pool, and all RRHs within the pool are logically connected to every BBU in the pool. Thus, a BBU-RRH switching algorithm needs to be developed as it is able to enhance the performance of such architecture while managing the resource efficiently. This work mainly focuses on developing a traffic profile prediction-based BBU-RRH switching algorithm using a real life dataset. In the literature, there are related works that have proposed algorithms to achieve this purpose. However some of the existing algorithms suffer from high switching complexity while others fall short in QoS provision. Therefore, this work develops a BBU-RRH algorithm that to enhance the QoS while reducing the switching complexity, with the aid of machine learning techniques. The algorithm developed consists of three parts. The first part consists of an efficient RRH clustering mechanism that determines which RRHs are associated with a specific BBU pool. The second part utilizesrecurrent neural networks (RNN) to predict the daily traffic profile of RRHs, so that a relatively accurate traffic profile prediction can be obtained to facilitate the switching algorithm. Finally, the third part comprises the BBU-RRH switching scheme that works in conjunction with the predicted traffic profile to make an informed decision about the associations between RRHs and BBUs within the BBU pool. The performance of the proposed algorithm has been evaluated through simulations. The simulation results show that the proposed algorithm reduces the number of BBUs used and therefore save on energy. In addition, the algorithm reduces the occurrence of congestion and failure states, and thus improve the quality of the service of the network. Finally, the developed switching algorithm also reduces the switching complexity when compared with existing algorithms

Study plan to identify long term national telecommunications need and priorities applying Delphi techniques (handbook)

A handbook that explains the basic Delphi methodology and discusses modified Delphi techniques is presented. The selection of communications experts to participate in a study, the construction of questionnaires on potential communications developments, and requisite technology is treated. No two modified Delphi studies were the same, which reflects the flexibility and adaptability of the technique. Each study must be specifically tailored to a particular case, and consists of seeking a consensus of opinion among experts about a particular subject and attendant conditions that may prevail in the future

Network virtualization in next generation cellular networks

The complexity of operation and management of emerging cellular networks significantly increases, as they evolve to correspond to increasing QoS needs, data rates and diversity of offered services. Thus critical challenges appear regarding their performance. At the same time, network sustainability pushes toward the utilization of haring Radio Access Network (RAN) infrastructure between Mobile Network Operators (MNOs). This requires advanced network management techniques which have to be developed based on characteristics of these networks and traffic demands. Therefore it is necessary to provide solutions enabling the creation of logically isolated network partitions over shared physical network infrastructure. Multiple heterogeneous virtual networks should simultaneously coexist and support resource aggregation so as to appear as a single resource to serve different traffic types on demand. Hence in this thesis, we study RAN virtualization and slicing solutions destined to tackle these challenges. In the first part, we present our approach to map virtual network elements onto radio resources of the substrate physical network, in a dense multi-tier LTE-A scenario owned by a MNO. We propose a virtualization solution at BS level, where baseband modules of distributed BSs, interconnected via logical point-to-point X2 interface, cooperate to reallocate radio resources on a traffic need basis. Our proposal enhances system performance by achieving 53% throughput gain compared with benchmark schemes without substantial signaling overhead. In the second part of the thesis, we concentrate on facilitating resource provisioning between multiple Virtual MNOs (MVNOs), by integrating the capacity broker in the 3GPP network management architecture with minimum set of enhancements. A MNO owns the network and provides RAN access on demand to several MVNOs. Furthermore we propose an algorithm for on-demand resource allocation considering two types of traffic. Our proposal achieves 50% more admitted requests without Service Level Agreement (SLA) violation compared with benchmark schemes. In the third part, we devise and study a solution for BS agnostic network slicing leveraging BS virtualization in a multi-tenant scenario. This scenario is composed of different traffic types (e.g., tight latency requirements and high data rate demands) along with BSs characterized by different access and transport capabilities (i.e., Remote Radio Heads, RRHs, Small Cells, SCs and future 5G NodeBs, gNBs with various functional splits having ideal and non-ideal transport network). Our solution achieves 67% average spectrum usage gain and 16.6% Baseband Unit processing load reduction compared with baseline scenarios. Finally, we conclude the thesis by providing insightful research challenges for future works.La complejidad de la operación y la gestión de las emergentes redes celulares aumenta a medida que evolucionan para hacer frente a las crecientes necesidades de calidad de servicio (QoS), las tasas de datos y la diversidad de los servicios ofrecidos. De esta forma aparecen desafíos críticos con respecto a su rendimiento. Al mismo tiempo, la sostenibilidad de la red empuja hacia la utilización de la infraestructura de red de acceso radio (RAN) compartida entre operadores de redes móviles (MNO). Esto requiere técnicas avanzadas de gestión de redes que deben desarrollarse en función de las características especiales de estas redes y las demandas de tráfico. Por lo tanto, es necesario proporcionar soluciones que permitan la creación de particiones de red aisladas lógicamente sobre la infraestructura de red física compartida. Para ello, en esta tesis, estudiamos las soluciones de virtualización de la RAN destinadas a abordar estos desafíos. En la primera parte de la tesis, nos centramos en mapear elementos de red virtual en recursos de radio de la red física, en un escenario LTE-A de múltiples niveles que es propiedad de un solo MNO. Proponemos una solución de virtualización a nivel de estación base (BS), donde los módulos de banda base de BSs distribuidas, interconectadas a través de la interfaz lógica X2, cooperan para reasignar los recursos radio en función de las necesidades de tráfico. Nuestra propuesta mejora el rendimiento del sistema al obtener un rendimiento 53% en comparación con esquemas de referencia. En la segunda parte de la tesis, nos concentramos en facilitar el aprovisionamiento de recursos entre muchos operadores de redes virtuales móviles (MVNO), al integrar el capacity broker en la arquitectura de administración de red 3GPP con un conjunto míinimo de mejoras. En este escenario, un MNO es el propietario de la red y proporciona acceso bajo demanda (en inglés on-demand) a varios MVNOs. Además, para aprovechar al máximo las capacidades del capacity broker, proponemos un algoritmo para la asignación de recursos bajo demanda, considerando dos tipos de tráfico con distintas características. Nuestra propuesta alcanza 50% más de solicitudes admitidas sin violación del Acuerdo de Nivel de Servicio (SLA) en comparación con otros esquemas. En la tercera parte de la tesis, estudiamos una solución para el slicing de red independiente del tipo de BS, considerando la virtualización de BS en un escenario de múltiples MVNOs (multi-tenants). Este escenario se compone de diferentes tipos de tráfico (por ejemplo, usuarios con requisitos de latencia estrictos y usuarios con altas demandas de velocidad de datos) junto con BSs caracterizadas por diferentes capacidades de acceso y transporte (por ejemplo, Remote Radio Heads, RRHs, Small cells, SC y 5G NodeBs, gNBs con varias divisiones funcionales que tienen una red de transporte ideal y no ideal). Nuestra solución logra una ganancia promedio de uso de espectro de 67% y una reducción de la carga de procesamiento de la banda base de 16.6% en comparación con escenarios de referencia. Finalmente, concluimos la tesis al proporcionando los desafíos y retos de investigación para trabajos futuros.Postprint (published version

Internet traffic volumes characterization and forecasting

Internet usage increases every year and the need to estimate the growth of the generated traffic has become a major topic. Forecasting actual figures in advance is essential for bandwidth allocation, networking design and investment planning. In this thesis novel mathematical equations are presented to model and to predict long-term Internet traffic in terms of total aggregating volume, globally and more locally. Historical traffic data from consecutive years have revealed hidden numerical patterns as the values progress year over year and this trend can be well represented with appropriate mathematical relations. The proposed formulae have excellent fitting properties over long-history measurements and can indicate forthcoming traffic for the next years with an exceptionally low prediction error. In cases where pending traffic data have already become available, the suggested equations provide more successful results than the respective projections that come from worldwide leading research. The studies also imply that future traffic strongly depends on the past activity and on the growth of Internet users, provided that a big and representative sample of pertinent data exists from large geographical areas. To the best of my knowledge this work is the first to introduce effective prediction methods that exclusively rely on the static attributes and the progression properties of historical values

Architectures and GPU-Based Parallelization for Online Bayesian Computational Statistics and Dynamic Modeling

Recent work demonstrates that coupling Bayesian computational statistics methods with dynamic models can facilitate the analysis of complex systems associated with diverse time series, including those involving social and behavioural dynamics. Particle Markov Chain Monte Carlo (PMCMC) methods constitute a particularly powerful class of Bayesian methods combining aspects of batch Markov Chain Monte Carlo (MCMC) and the sequential Monte Carlo method of Particle Filtering (PF). PMCMC can flexibly combine theory-capturing dynamic models with diverse empirical data. Online machine learning is a subcategory of machine learning algorithms characterized by sequential, incremental execution as new data arrives, which can give updated results and predictions with growing sequences of available incoming data. While many machine learning and statistical methods are adapted to online algorithms, PMCMC is one example of the many methods whose compatibility with and adaption to online learning remains unclear. In this thesis, I proposed a data-streaming solution supporting PF and PMCMC methods with dynamic epidemiological models and demonstrated several successful applications. By constructing an automated, easy-to-use streaming system, analytic applications and simulation models gain access to arriving real-time data to shorten the time gap between data and resulting model-supported insight. The well-defined architecture design emerging from the thesis would substantially expand traditional simulation models' potential by allowing such models to be offered as continually updated services. Contingent on sufficiently fast execution time, simulation models within this framework can consume the incoming empirical data in real-time and generate informative predictions on an ongoing basis as new data points arrive. In a second line of work, I investigated the platform's flexibility and capability by extending this system to support the use of a powerful class of PMCMC algorithms with dynamic models while ameliorating such algorithms' traditionally stiff performance limitations. Specifically, this work designed and implemented a GPU-enabled parallel version of a PMCMC method with dynamic simulation models. The resulting codebase readily has enabled researchers to adapt their models to the state-of-art statistical inference methods, and ensure that the computation-heavy PMCMC method can perform significant sampling between the successive arrival of each new data point. Investigating this method's impact with several realistic PMCMC application examples showed that GPU-based acceleration allows for up to 160x speedup compared to a corresponding CPU-based version not exploiting parallelism. The GPU accelerated PMCMC and the streaming processing system can complement each other, jointly providing researchers with a powerful toolset to greatly accelerate learning and securing additional insight from the high-velocity data increasingly prevalent within social and behavioural spheres. The design philosophy applied supported a platform with broad generalizability and potential for ready future extensions. The thesis discusses common barriers and difficulties in designing and implementing such systems and offers solutions to solve or mitigate them

5G network slicing for rural connectivity: multi-tenancy in wireless networks

As the need for wireless broadband continues to grow around the world, there is an increasing focus to minimise the existing digital divide and ensuring that everyone receives high-quality internet services, especially the inhabitants of rural areas. As a result, different technological solutions are being studied and trialled for improving rural connectivity, such as 5G with dynamic spectrum access. One of the architectures of 5G is network slicing, which supports network virtualisation and consists of independent logical networks, called slices, on the 5G network. Network slicing supports the multi-tenancy of different operators on the same physical network, and this feature is known as neutral host networks (NHN). It allows multiple operators to co-exist on the same physical network but on different virtual networks to serve end users. Generally, the 5G NHN deployment is handled by an infrastructure provider (InP), who could be a mobile network operator (MNO), an Internet service provider, a third-party operator, etc. At the same time, potential tenants would lease slices from the InP. The NHN strategy would help reduce resource duplication and increase the utilisation of existing resources. The existing research into NHN for small cells, in-building connectivity solutions, and other deployment scenarios help to understand the technological and business requirements. End-to-end sharing across operators to provide services to their end users is another innovative application of 5G NHN that has been tested for dense areas. Meanwhile, the feasibility and policy impact of NHN is not studied extensively for the rural scenario. The research in this thesis examines the use of NHN in macro- and small-cell networks for 5G communication systems to minimise the digital divide, with a special focus on rural areas. The study also presents and analyses the 5G multi-tenancy system design for the rural wireless scenario, focusing mainly on exploring suitable business cases through network economics, techno-economic study, and game theory analysis. The results obtained from the study, such as cost analysis, business models, sensitivity analysis, and pricing strategies, help in formulating the policy on infrastructure sharing to improve rural connectivity. The contributions of the thesis are useful for stakeholders and policymakers to assess the suitability of the rural 5G NHN by exploring state-of-the-art technologies, techno-economic analysis, sensitivity analysis, newer business models, investment assessment, cost allocation, and risk sharing. Initially, the research gap is highlighted through the extensive literature review and stakeholders’ views on rural connectivity collected from discussions with them. First, the in-depth discussion on the network economics of the rural 5G NHN includes the study of potential future scenarios, value network configurations, spectrum access strategy models, and business models. Secondly, the techno-economic analysis studies the key performance indicators (KPI), cost analysis, return on investment, net present value, and sensitivity analysis, with the application for the rural parts of the UK and India. Finally, the game theory framework includes the study of strategic interaction among the two key stakeholders, InP and the MNO, using models such as investment games and pricing strategies during multi-tenancy. The research concludes by presenting the contribution towards the knowledge and future work.As the need for wireless broadband continues to grow around the world, there is an increasing focus to minimise the existing digital divide and ensuring that everyone receives high-quality internet services, especially the inhabitants of rural areas. As a result, different technological solutions are being studied and trialled for improving rural connectivity, such as 5G with dynamic spectrum access. One of the architectures of 5G is network slicing, which supports network virtualisation and consists of independent logical networks, called slices, on the 5G network. Network slicing supports the multi-tenancy of different operators on the same physical network, and this feature is known as neutral host networks (NHN). It allows multiple operators to co-exist on the same physical network but on different virtual networks to serve end users. Generally, the 5G NHN deployment is handled by an infrastructure provider (InP), who could be a mobile network operator (MNO), an Internet service provider, a third-party operator, etc. At the same time, potential tenants would lease slices from the InP. The NHN strategy would help reduce resource duplication and increase the utilisation of existing resources. The existing research into NHN for small cells, in-building connectivity solutions, and other deployment scenarios help to understand the technological and business requirements. End-to-end sharing across operators to provide services to their end users is another innovative application of 5G NHN that has been tested for dense areas. Meanwhile, the feasibility and policy impact of NHN is not studied extensively for the rural scenario. The research in this thesis examines the use of NHN in macro- and small-cell networks for 5G communication systems to minimise the digital divide, with a special focus on rural areas. The study also presents and analyses the 5G multi-tenancy system design for the rural wireless scenario, focusing mainly on exploring suitable business cases through network economics, techno-economic study, and game theory analysis. The results obtained from the study, such as cost analysis, business models, sensitivity analysis, and pricing strategies, help in formulating the policy on infrastructure sharing to improve rural connectivity. The contributions of the thesis are useful for stakeholders and policymakers to assess the suitability of the rural 5G NHN by exploring state-of-the-art technologies, techno-economic analysis, sensitivity analysis, newer business models, investment assessment, cost allocation, and risk sharing. Initially, the research gap is highlighted through the extensive literature review and stakeholders’ views on rural connectivity collected from discussions with them. First, the in-depth discussion on the network economics of the rural 5G NHN includes the study of potential future scenarios, value network configurations, spectrum access strategy models, and business models. Secondly, the techno-economic analysis studies the key performance indicators (KPI), cost analysis, return on investment, net present value, and sensitivity analysis, with the application for the rural parts of the UK and India. Finally, the game theory framework includes the study of strategic interaction among the two key stakeholders, InP and the MNO, using models such as investment games and pricing strategies during multi-tenancy. The research concludes by presenting the contribution towards the knowledge and future work

The implementation of energy sharing using a system of systems approach

There is an increasing demand for renewable energy and consumers need more procurement options to meet their needs. Energy sharing provides a peer-to-peer (P2P) marketplace where prosumer electricity is redistributed to fellow energy-sharing community participants. This redistribution of prosumer electricity provides consumers with additional electricity suppliers, while also decreasing the load on the utility company. Though significant progress has been made regarding research and implementation of energy sharing, there is still room for growth when evaluating energy-sharing communities and defining appropriate community coordination based on end-user needs. The first contribution in this work identified nine characteristics of energy-sharing communities as a decentralized complex adaptive system of systems (DCASoS). Considering each characteristic before determining community coordination is vital to ensure ample participation within the energy-sharing community. The second contribution was the exploration of a two-stage stochastic programming model as an alternative to the classic energy distribution business model. The third contribution compares three behavioral theories to identify the best fitting model to predict interest in participating in an energysharing community. This research provides companies with foundational knowledge to develop an energy-sharing community that both fulfills end-user satisfaction and increases robustness of electricity distribution business models --Abstract, page iv